IA-QA — 152 QA & Dev Tools for AI Agents
REMOTE · WWW.IA-QA.COM
152 free QA tools for agents: injection scan, evals, RAG, web audits. Smaller packs: /mcp/{pack}.
Available components
Last scanned (11 hours ago)
Scores higher than 97% of remote servers
Reachable. Last reached 5 Nov 2026.
Category Testing and QA
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security83
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- No authorisation is required to call this server. Nothing on it is marked destructive, but we can't show that an anonymous caller can only read: full credit needs the server to list at least one tool and every tool to be verifiably read-only. We use a best-effort check of tool names and descriptions to tell read-only tools from tools that change things, so some genuinely read-only tools may not get full credit. Names that start with a read verb (get, list, read, search and similar) and accurate readOnlyHint annotations help. Meet that to earn full credit, or require authorisation. See how to fix → View diagnostics → Partial
- HTTPS is enforced; there's no plaintext access path. View diagnostics → Pass
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC is configured correctly; the domain's records validate against the full chain to the root. View diagnostics → Pass
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability86
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 22973 tokens (~151/item across 152 items; 152 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Tools include usage examples.Pass
Stability & Change Management100
- Stability check failed: schema churn in the 30 days we've observed: 0 tool removals, 1 breaking changes, 0 auth/transport breaks, 0 additions. See how to fix → Fail
Tool Coverage99
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 97% of tool parameters carry a description.Partial
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 5 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "run_eval_contract" implies "eval" and declares readOnlyHint instead, contradicting what its own name says it does. See how to fix → Fail
- An AI judge read all 153 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Checks we ran (13)
- TLS certificate
- Authorization
- HTTPS
- HSTS header
- DNSSEC
- Transport
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Does not apply to this hosted endpoint (2)
- Supply Chain SecurityScored on package channels only. This is a hosted endpoint, so there is nothing to install or inspect.
- Provenance & TransparencyScored on package channels only. This is a hosted endpoint, so there is nothing to install or inspect.
The score does not measure everything. What the score does not measure →
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 0
- The server rewrote its instructions, which are the text every model session reads security
- Tool “cors_test” rewrote its description, which is the text the model reads security
- Tool “mcp_server_evaluate” rewrote its description, which is the text the model reads security
- MCP protocol: Implements a current MCP spec version (2026-07-28). functional
- MCP protocol version: 2025-11-25 → 2026-07-28 functional
- “mcp_server_evaluate” added an optional parameter “test_tool_args” cosmetic
- “mcp_server_evaluate” reworded the description of “test_tool_name” cosmetic
- 4 Nov 26 0
- Tool “find_tool” rewrote its description, which is the text the model reads security
- Tool “rate_tool” rewrote its description, which is the text the model reads security
- Tool “run_eval_contract” rewrote its description, which is the text the model reads security
- “run_eval_contract” reworded the description of “inline_contract” cosmetic
- 3 Nov 26 0
- Tool “compare_models” rewrote its description, which is the text the model reads security
- Tool “estimate_llm_cost” rewrote its description, which is the text the model reads security
- Tool “llm_fit_finder” rewrote its description, which is the text the model reads security
- Tool “llm_generate” rewrote its description, which is the text the model reads security
- Tool “token_budget_calculator” rewrote its description, which is the text the model reads security
- “compare_models” reworded the description of “models” cosmetic
- “context_window_check” reworded the description of “model” cosmetic
- “llm_fit_finder” reworded the description of “top_n” cosmetic
- “llm_fit_finder” reworded the description of “use_case” cosmetic
- “llm_generate” reworded the description of “model” cosmetic
- “shield_analyze” reworded the description of “model” cosmetic
- “token_budget_calculator” reworded the description of “model” cosmetic
- “compare_models” dropped the optional parameter “use_case” cosmetic
- 1 Nov 26 +2
- Stability: pass → fail ▼ security
- A breaking change shipped without a version bump: still 1.0.0 ▼ security
- The server rewrote its instructions, which are the text every model session reads security
- Tool “detect_secrets” rewrote its description, which is the text the model reads security
- Tool “generate_ci_workflow” rewrote its description, which is the text the model reads security
- Tool “jira_to_test_suite” rewrote its description, which is the text the model reads security
- Tool “mcp_schema_lint” rewrote its description, which is the text the model reads security
- Tool “metamorphic_check” rewrote its description, which is the text the model reads security
- Tool “multimodal_eval_guide” rewrote its description, which is the text the model reads security
- Tool “prompt_injection_scan” rewrote its description, which is the text the model reads security
- Tool “rerank_evaluate” rewrote its description, which is the text the model reads security
- Tool “run_eval_contract” rewrote its description, which is the text the model reads security
- Tool “run_vlm_test_suite” rewrote its description, which is the text the model reads security
- Tool “run_vlm_test_suite_batch” rewrote its description, which is the text the model reads security
- Tool “secret_scan” rewrote its description, which is the text the model reads security
- Tool “shield_analyze” rewrote its description, which is the text the model reads security
- Tool “test_skill” rewrote its description, which is the text the model reads security
- Tool “validate_agent_trajectory” rewrote its description, which is the text the model reads security
- “secret_scan” changed the type of “input”: string → string|array ▼ functional
- Schema quality: fail → pass ▲ functional
- “hallucination_check” added an optional parameter “cases” cosmetic
- “json_schema_generate” added an optional parameter “samples” cosmetic
- “generate_ci_workflow” reworded the description of “app_base_url” cosmetic
- “generate_ci_workflow” reworded the description of “app_start_command” cosmetic
- “generate_ci_workflow” reworded the description of “cli_tools” cosmetic
- “generate_ci_workflow” reworded the description of “contract_path” cosmetic
- “generate_ci_workflow” reworded the description of “cron” cosmetic
- “generate_ci_workflow” reworded the description of “fail_on_fail” cosmetic
- “generate_ci_workflow” reworded the description of “gate” cosmetic
- “generate_ci_workflow” reworded the description of “min_score” cosmetic
- “generate_ci_workflow” reworded the description of “node_version” cosmetic
- “generate_ci_workflow” reworded the description of “provider” cosmetic
- “generate_ci_workflow” reworded the description of “triggers” cosmetic
- “generate_ci_workflow” reworded the description of “workflow_name” cosmetic
- “hallucination_check” reworded the description of “answer” cosmetic
- “hallucination_check” reworded the description of “context” cosmetic
- “hallucination_check” reworded the description of “strict” cosmetic
- “jira_to_test_suite” reworded the description of “api_key” cosmetic
- “jira_to_test_suite” reworded the description of “confluence_pages” cosmetic
- “jira_to_test_suite” reworded the description of “issue” cosmetic
- “jira_to_test_suite” reworded the description of “issue_key” cosmetic
- “jira_to_test_suite” reworded the description of “jira_base_url” cosmetic
- “jira_to_test_suite” reworded the description of “jira_email” cosmetic
- “jira_to_test_suite” reworded the description of “jira_token” cosmetic
- “jira_to_test_suite” reworded the description of “max_tokens” cosmetic
- “jira_to_test_suite” reworded the description of “model” cosmetic
- “json_schema_generate” reworded the description of “required_all” cosmetic
- “metamorphic_check” reworded the description of “api_key” cosmetic
- “metamorphic_check” reworded the description of “base” cosmetic
- “metamorphic_check” reworded the description of “baseline_guard” cosmetic
- “metamorphic_check” reworded the description of “mode” cosmetic
- “metamorphic_check” reworded the description of “require_all” cosmetic
- “metamorphic_check” reworded the description of “thresholds” cosmetic
- “metamorphic_check” reworded the description of “variants” cosmetic
- “multimodal_eval_guide” reworded the description of “action” cosmetic
- “multimodal_eval_guide” reworded the description of “api_key” cosmetic
- “multimodal_eval_guide” reworded the description of “clip” cosmetic
- “multimodal_eval_guide” reworded the description of “fid” cosmetic
- “multimodal_eval_guide” reworded the description of “generated_images” cosmetic
- “multimodal_eval_guide” reworded the description of “image_base64” cosmetic
- “multimodal_eval_guide” reworded the description of “image_mime_type” cosmetic
- “multimodal_eval_guide” reworded the description of “image_url” cosmetic
- “multimodal_eval_guide” reworded the description of “metric” cosmetic
- “multimodal_eval_guide” reworded the description of “model” cosmetic
- “multimodal_eval_guide” reworded the description of “real_images” cosmetic
- “multimodal_eval_guide” reworded the description of “score” cosmetic
- “multimodal_eval_guide” reworded the description of “system_prompt” cosmetic
- “multimodal_eval_guide” reworded the description of “test_cases” cosmetic
- “multimodal_eval_guide” reworded the description of “text” cosmetic
- “multimodal_eval_guide” reworded the description of “vqa” cosmetic
- “rerank_evaluate” reworded the description of “api_key” cosmetic
- “rerank_evaluate” reworded the description of “passages” cosmetic
- “rerank_evaluate” reworded the description of “query” cosmetic
- “rerank_evaluate” reworded the description of “threshold” cosmetic
- “rerank_evaluate” reworded the description of “top_k” cosmetic
- “response_quality_score” reworded the description of “expected_keywords” cosmetic
- “run_eval_contract” reworded the description of “api_keys” cosmetic
- “run_eval_contract” reworded the description of “contract_path” cosmetic
- “run_eval_contract” reworded the description of “inline_contract” cosmetic
- “run_vlm_test_suite” reworded the description of “api_key” cosmetic
- “run_vlm_test_suite” reworded the description of “image_base64” cosmetic
- “run_vlm_test_suite” reworded the description of “image_mime_type” cosmetic
- “run_vlm_test_suite” reworded the description of “image_url” cosmetic
- “run_vlm_test_suite” reworded the description of “model” cosmetic
- “run_vlm_test_suite” reworded the description of “system_prompt” cosmetic
- “run_vlm_test_suite” reworded the description of “test_cases” cosmetic
- “run_vlm_test_suite” reworded the description of “threshold” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “api_keys” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “image_base64” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “image_mime_type” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “image_url” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “models” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “system_prompt” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “test_cases” cosmetic
- “run_vlm_test_suite_batch” reworded the description of “threshold” cosmetic
- “secret_scan” reworded the description of “input” cosmetic
- “secret_scan” reworded the description of “types” cosmetic
- “shield_analyze” reworded the description of “model” cosmetic
- “shield_analyze” reworded the description of “prompt” cosmetic
- “shield_analyze” reworded the description of “response” cosmetic
- “shield_analyze” reworded the description of “rules” cosmetic
- “shield_analyze” reworded the description of “source” cosmetic
- “test_skill” reworded the description of “api_key” cosmetic
- “test_skill” reworded the description of “dir_name” cosmetic
- “test_skill” reworded the description of “lint_only” cosmetic
- “test_skill” reworded the description of “model” cosmetic
- “test_skill” reworded the description of “other_skills” cosmetic
- “test_skill” reworded the description of “scenario_count” cosmetic
- “test_skill” reworded the description of “scenarios” cosmetic
- “validate_agent_trajectory” reworded the description of “assertions” cosmetic
- “validate_agent_trajectory” reworded the description of “format” cosmetic
- “validate_agent_trajectory” reworded the description of “trace” cosmetic
- “hallucination_check” made “answer” optional cosmetic
- “hallucination_check” made “context” optional cosmetic
- 29 Oct 26 0
- Tool “llm_generate” rewrote its description, which is the text the model reads security
- “llm_generate” reworded the description of “model” cosmetic
- 28 Oct 26 0
- Tool “ab_test_report” rewrote its description, which is the text the model reads security
- Tool “function_call_validate” rewrote its description, which is the text the model reads security
- Tool “json_schema_validate” rewrote its description, which is the text the model reads security
- Tool “prompt_injection_scan” rewrote its description, which is the text the model reads security
- Tool “rate_tool” rewrote its description, which is the text the model reads security
- Tool “redact_pii” rewrote its description, which is the text the model reads security
- Tool “toxicity_scan” rewrote its description, which is the text the model reads security
- Tool “validate_agent_trajectory” rewrote its description, which is the text the model reads security
- “function_call_validate” reworded the description of “allowed_functions” cosmetic
- “redact_pii” reworded the description of “types” cosmetic
- 26 Oct 26 0
- “compare_models” reworded the description of “models” cosmetic
- “jira_to_test_suite” reworded the description of “model” cosmetic
- “llm_generate” reworded the description of “model” cosmetic
- “model_info” reworded the description of “model” cosmetic
- “token_budget_calculator” reworded the description of “model” cosmetic
5 cosmetic changes on this day. Switch on “Show cosmetic changes” to see them.
- 24 Oct 26 0
- Tool “mcp_server_evaluate” rewrote its description, which is the text the model reads security
- Tool “mcp_server_health_check” rewrote its description, which is the text the model reads security
- “mcp_server_evaluate” reworded the description of “test_tool_name” cosmetic
- “mcp_server_evaluate” reworded the description of “url” cosmetic
- “mcp_server_health_check” reworded the description of “manifest” cosmetic
- “mcp_server_health_check” reworded the description of “strict” cosmetic
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Probed https://www.ia-qa.com/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=www.ia-qa.com | CN=YR2,O=Let's Encrypt,C=US | 10 Oct 2026 | 8 Jan 2027 | RSA 2048 | SHA256-RSA | 660e1d6981514e1320131b13dbf4a910775 |
| SANs: www.ia-qa.com | ||||||
| CN=YR2,O=Let's Encrypt,C=US (CA) | CN=Root YR,O=ISRG,C=US | 3 Sept 2025 | 2 Sept 2028 | RSA 2048 | SHA256-RSA | 4ebd24947e24d394802d84a52fd5b319 |
| CN=Root YR,O=ISRG,C=US (CA) | CN=ISRG Root X1,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | RSA 4096 | SHA256-RSA | f24b6d17f9d9ad7cb1c9fea78782699f |
Background: What to check on a remote MCP endpoint →
DNSSEC secure
Validation of www.ia-qa.com. — Secure
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | Trust anchor | 20326, 38696 | 8, 8 | Verified |
| com. | DS published | 19718 | 13 | Verified |
| ia-qa.com. | DS published | 52852 | 8 | Verified |
| www.ia-qa.com. | Verified address RRset verified with the apex keys |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
| Header | Value |
|---|---|
| strict-transport-security | max-age=63072000; includeSubDomains; preload |
| content-security-policy | default-src 'self';script-src 'self' 'unsafe-inline' 'unsafe-eval' https://www.googletagmanager.com;style-src 'self' 'unsafe-inline' https://fonts.googleapis.com;img-src 'self' data: blob: https:;font-src 'self' https://fonts.gstatic.com data:;connect-src 'self' https: wss: http://localhost:11434 data:;media-src 'self' blob:;object-src 'none';frame-ancestors 'self';base-uri 'self';form-action 'self';script-src-attr 'none';upgrade-insecure-requests |
| x-content-type-options | nosniff |
| x-frame-options | SAMEORIGIN |
| referrer-policy | no-referrer |
| permissions-policy | camera=(), microphone=(), geolocation=(), payment=(), usb=(), display-capture=() |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://www.ia-qa.com/mcp | Verified | 200 | |
| http (plaintext) | http://www.ia-qa.com/mcp | HTTPS enforced | 301 | https://www.ia-qa.com/mcp |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
sort_lines ~129
Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries.
| Name | Type | Req | Description |
|---|---|---|---|
| filter | string | – | For "filter": keep lines containing this substring (case-insensitive) |
| input | string | yes | Multi-line text to process |
| operation | string | – | "sort" (default), "sort_desc", "reverse", "deduplicate", "unique_sort", "filter" |
| remove_empty | boolean | – | Remove empty lines (default: true) |
| trim | boolean | – | Trim whitespace from each line (default: true) |
| Name | Type | Req | Description |
|---|---|---|---|
| line_count | number | – | – |
| original_count | number | – | – |
| removed | number | – | – |
| result | string | – | – |
{"input":"b\na\nb\nc","operation":"deduplicate"}split_chunks ~85
Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_tokens | number | yes | Maximum tokens per chunk (10–8000) |
| input | string | yes | Text to split into chunks |
| overlap | number | – | Token overlap between consecutive chunks (default: 0) |
| Name | Type | Req | Description |
|---|---|---|---|
| chunk_count | number | – | – |
| chunks | array | – | – |
| overlap_tokens | – | – | – |
| tokens_per_chunk | – | – | – |
{"chunk_tokens":12,"input":"Refunds are accepted within 30 days. Damaged items are replaced for free. Contact support to start a return.","overlap":2}ssl_certificate_check ~122
Analyse the SSL/TLS certificate of any HTTPS host. Returns certificate subject, issuer, validity dates, days until expiry, protocol version, cipher suite, key exchange info, and an overall grade (A+, A, B, C, F). Detects expired, self-signed, and weak certificates. Use this to audit TLS posture before production deployment or during security reviews.
| Name | Type | Req | Description |
|---|---|---|---|
| host | string | yes | Hostname to check (e.g. example.com). Do not include https:// prefix. |
| port | number | – | Port number (default: 443) |
| Name | Type | Req | Description |
|---|---|---|---|
| cipher | object | – | – |
| days_until_expiry | number | – | – |
| grade | string | – | – |
| host | string | – | – |
| is_expired | boolean | – | – |
| is_self_signed | boolean | – | – |
| issuer | object | – | – |
| issues | array | – | – |
| protocol | string | – | – |
| subject | object | – | – |
| valid_from | string | – | – |
| valid_to | string | – | – |
{"host":"www.ia-qa.com"}strip_markdown ~76
Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped documentation, README files, or user content into an LLM prompt to eliminate redundant markup tokens and reduce cost.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | Markdown text to convert to plain text |
| Name | Type | Req | Description |
|---|---|---|---|
| original_length | number | – | – |
| stripped_length | number | – | – |
| text | – | – | – |
{"input":"# Title\n**bold** and [link](http://x.com) and `code`\n- item"}system_prompt_builder ~136
Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prompt with token estimate.
| Name | Type | Req | Description |
|---|---|---|---|
| constraints | array | – | Rules and constraints to follow |
| examples | string | – | Brief examples to include |
| language | string | – | Response language (e.g. "French") |
| output_format | string | – | Expected output format description |
| role | string | yes | Role/persona (e.g. "Senior QA Engineer", "JSON extraction assistant") |
| task | string | – | Main task or objective |
| tone | string | – | Communication tone |
| Name | Type | Req | Description |
|---|---|---|---|
| sections | object | – | – |
| system_prompt | – | – | – |
| token_estimate | number | – | – |
{"constraints":["no PII"],"output_format":"json","role":"support agent"}test_skill ~323
Test a SKILL.md (Agent Skills format). (1) lint: the spec's frontmatter rules, no model — an error returns BLOCK; gate on lint.valid. (2) triggering: a model sees only the names + descriptions of this skill and its neighbours and picks which to load (temperature 0); trigger_accuracy PASS ≥80, FIX ≥50, else BLOCK. Your own scenarios give a reproducible gate; generated ones are a signal. Unanswered requests make the run INCONCLUSIVE; so do scenarios left when a call reaches its 22 s budget (split long runs). Default model: Groq on the server key.
| Name | Type | Req | Description |
|---|---|---|---|
| api_key | string | – | Key for the chosen model's provider; not needed for the default. |
| dir_name | string | – | Folder holding the SKILL.md; name must match it. |
| lint_only | boolean | – | Format check only: no model, no key. The CI gate for a SKILL.md. |
| model | string | – | e.g. gpt-4o-mini. Default openai/gpt-oss-20b (Groq, server key). |
| other_skills | array | – | The skills installed next to this one. Default: six generic built-ins. |
| scenario_count | number | – | Requests to generate when scenarios is omitted (default 6). |
| scenarios | array | – | Fixed requests, near misses included (should_trigger false). Max 20. |
| skill_md | string | yes | Full content of the SKILL.md file to test, YAML frontmatter included. |
| Name | Type | Req | Description |
|---|---|---|---|
| lint | object | – | – |
| scenarios | array | – | – |
| scenarios_source | string | – | – |
| score | number | – | – |
| step_adherence | number | – | – |
| trigger_accuracy | number | – | – |
| verdict | string | – | – |
{"skill_md":"---\nname: json-formatter\ndescription: Pretty-print a JSON string the user pastes.\n---\nFormat the JSON with 2-space indentation."}text_stats ~119
Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated reading time in minutes. Sentence counting is abbreviation-aware — titles (Mr., Dr.), acronyms (U.S., i.e., p.m.), initials, decimals, URLs and emails do not end a sentence, and a text with no terminal punctuation still counts as one. Use for validating form field lengths, evaluating LLM output verbosity, or content auditing.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | The text to analyse |
| Name | Type | Req | Description |
|---|---|---|---|
| chars | – | – | – |
| chars_no_space | – | – | – |
| lines | – | – | – |
| paragraphs | – | – | – |
| reading_time_minutes | – | – | – |
| sentences | – | – | – |
| words | – | – | – |
{"input":"One two three.\nFour five!\n\nSix?"}timestamp_convert ~90
Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the current time. Returns iso, unix_s, unix_ms, utc, date, and time fields.
| Name | Type | Req | Description |
|---|---|---|---|
| input | – | – | Unix timestamp (number, seconds or ms) or ISO date string. Omit to get the current time. |
| Name | Type | Req | Description |
|---|---|---|---|
| date | string | – | – |
| iso | string | – | – |
| time | string | – | – |
| unix_ms | number | – | – |
| unix_s | number | – | – |
| utc | string | – | – |
{"input":0}token_budget_calculator ~194
Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds the model's context window (any model from list_llm_models).
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | – | Actual context text (will estimate tokens) |
| context_tokens | number | – | Token count for RAG context / documents |
| expected_output_tokens | number | – | Expected max output tokens |
| model | string | yes | Model id from list_llm_models (e.g. gpt-6.1-sol, claude-sonnet-5-5, gemini-3.8-flash) |
| system_prompt | string | – | Actual system prompt text (will estimate tokens) |
| system_prompt_tokens | number | – | Token count for system prompt |
| user_input | string | – | Actual user input text (will estimate tokens) |
| user_input_tokens | number | – | Token count for user message |
| Name | Type | Req | Description |
|---|---|---|---|
| breakdown | object | – | – |
| context_window | – | – | – |
| fits_in_window | – | – | – |
| model | – | – | – |
| remaining_tokens | – | – | – |
| utilization_percent | – | – | – |
| warnings | – | – | – |
{"context_tokens":1000,"expected_output_tokens":500,"model":"gpt-4o"}toxicity_scan ~190
Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term + predicate), not a semantic classifier — returns per-category risk plus the named rules that fired, so every finding can be checked. Languages: English for every category; French for insults, coarse language, direct threats and self-harm intent; anything else reads clean, so clean is not proof of safety. Text is also matched after undoing leetspeak and lookalike letters. Self-harm is critical only on a first-person intent — a mention ("call 988 if you have suicidal thoughts") is medium. Useful for LLM safety guardrail testing and triage; signal-only, not a calibrated CI gate.
| Name | Type | Req | Description |
|---|---|---|---|
| categories | array | – | Categories to check (default: all) |
| text | string | yes | Text to scan |
| Name | Type | Req | Description |
|---|---|---|---|
| categories_checked | number | – | – |
| method | string | – | – |
| overall_risk | – | – | – |
| results | – | – | – |
| text_length | number | – | – |
{"text":"Ce contrat est conclu avec un partenaire."}transform_json_array ~312
Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), uniq_by (field). Useful for processing MCP tool results and LLM structured outputs.
| Name | Type | Req | Description |
|---|---|---|---|
| field | string | – | Field to operate on (for sort_by, group_by, count_by, uniq_by, filter) |
| fields | string | – | Comma-separated field list for "pluck" (e.g. "id,name,email") |
| filter_op | string | – | For "filter": "==" | "!=" | ">" | ">=" | "<" | "<=" | "contains" | "exists" | "!exists" |
| filter_value | string | – | For "filter": value to compare against |
| input | string|object|array | yes | The JSON containing an array (or an object with an array at `path`) — a JSON string, or the value itself. |
| n | number | – | For first_n / last_n: number of items |
| operation | string | yes | Operation: "pluck", "filter", "sort_by", "group_by", "count_by", "uniq_by", "reverse", "first_n", "last_n", "flatten" |
| path | string | – | Optional dot-notation path to the array within the JSON object (e.g. "data.items") |
| sort_order | string | – | For sort_by: "asc" (default) or "desc" |
| Name | Type | Req | Description |
|---|---|---|---|
| count | number | – | – |
| field | – | – | – |
| fields | – | – | – |
| group_count | number | – | – |
| operation | string | – | – |
| order | – | – | – |
| removed | number | – | – |
| removed_duplicates | number | – | – |
| result | – | – | – |
| total | number | – | – |
| unique_values | number | – | – |
{"field":"v","input":[{"v":2},{"v":10},{"v":1}],"operation":"sort_by","sort_order":"desc"}truncate_to_tokens ~115
Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the start (useful for keeping recent conversation history). Reports whether truncation occurred and the estimated token count.
| Name | Type | Req | Description |
|---|---|---|---|
| from_end | boolean | – | Keep the end of the text instead of the start (default: false) |
| input | string | yes | Text to truncate |
| max_tokens | number | yes | Maximum number of tokens to keep |
| Name | Type | Req | Description |
|---|---|---|---|
| original_tokens_estimate | – | – | – |
| text | – | – | – |
| tokens_estimate | – | – | – |
| truncated | boolean | – | – |
{"input":"short","max_tokens":50}unescape_html ~81
Convert HTML entities (&, <, >, ", ', and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email content, or legacy database fields before passing to an LLM or displaying to users.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | HTML-encoded string to unescape |
| Name | Type | Req | Description |
|---|---|---|---|
| unescaped | – | – | – |
{"input":"\u0026#128512; \u0026#x1F600;"}url_decode ~49
Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | URL-encoded string to decode |
| Name | Type | Req | Description |
|---|---|---|---|
| decoded | – | – | – |
{"input":"a%20b%26c"}url_encode ~76
Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injection and malformed URLs.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | String to URL-encode |
| mode | string | – | "component" (default) or "full" for encodeURI behavior |
| Name | Type | Req | Description |
|---|---|---|---|
| encoded | – | – | – |
{"input":"a b\u0026c=d/é"}validate_agent_trajectory ~183
Run declarative assertions on an agent trace (OpenAI tool calls, Anthropic tool_use, LangChain run trees, ReAct logs). Deterministic, no LLM. Assertions: order (tool A before B), must_call, must_not_call, max/min calls, no_error, recovery (a successful step after the last error), no_injection, no_pii. A step is errored when the trace marks it (is_error, status error/failed, success:false) or its text starts with Error:/Traceback — prose is never keyword-scanned. Verdict PASS / FAIL / INCONCLUSIVE (0 steps parsed, never PASS).
| Name | Type | Req | Description |
|---|---|---|---|
| assertions | array | yes | – |
| format | string | – | Default auto. |
| trace | – | yes | The trace: JSON (messages array, run tree) or a Thought/Action/Observation text log. |
| Name | Type | Req | Description |
|---|---|---|---|
| results | array | – | – |
| steps | array | – | – |
| summary | object | – | – |
| verdict | string | – | – |
| warnings | array | – | – |
{"assertions":[{"tool":"search","type":"must_call"}],"trace":[{"role":"assistant","tool_calls":[{"function":{"arguments":"{}","name":"search"}}]}]}validate_email ~63
Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — use this to avoid bounces and malformed data.
| Name | Type | Req | Description |
|---|---|---|---|
| string | yes | Email address to validate |
| Name | Type | Req | Description |
|---|---|---|---|
| – | – | – | |
| reason | string | – | – |
| valid | boolean | – | – |
{"email":"john.doe@example.com"}validate_mcp_response ~225
Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSON result and a set of checks to perform.
| Name | Type | Req | Description |
|---|---|---|---|
| actual_latency | number | – | Actual measured latency in ms (from the call) |
| expected_type | string | – | Expected top-level type: "object", "array", "string", "number" |
| forbidden_keys | string | – | Comma-separated list of keys that MUST NOT exist (e.g. "password, secret, token") |
| max_response_ms | number | – | Maximum acceptable latency in ms (will be compared if provided) |
| max_size_bytes | number | – | Maximum acceptable response size in bytes |
| min_items | number | – | If response is an array, minimum number of items expected |
| required_keys | string | – | Comma-separated list of keys that MUST exist in the response (dot-notation for nested: "data.id, data.name") |
| response | string | yes | The MCP tool result as a JSON string to validate |
| Name | Type | Req | Description |
|---|---|---|---|
| checks | – | – | – |
| failed | number | – | – |
| passed | number | – | – |
| total | number | – | – |
| verdict | string | – | – |
{"forbidden_keys":"password","response":"{\"user\":{\"name\":\"x\"}}"}validate_url ~47
Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | URL to validate and parse |
| Name | Type | Req | Description |
|---|---|---|---|
| full | – | – | – |
| hash | – | – | – |
| hostname | – | – | – |
| origin | – | – | – |
| pathname | – | – | – |
| port | – | – | – |
| protocol | – | – | – |
| query_params | – | – | – |
| search | – | – | – |
| valid | boolean | – | – |
{"input":"https://www.ia-qa.com/mcp?x=1#y"}vector_quantize ~105
Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.
| Name | Type | Req | Description |
|---|---|---|---|
| bits | number | – | Quantization bits: 8 (int8, default) or 4 (int4) |
| vector | array | yes | Float32 vector to quantize |
| Name | Type | Req | Description |
|---|---|---|---|
| bits | – | – | – |
| compression_ratio | string | – | – |
| dimension | number | – | – |
| mse | number | – | – |
| offset | number | – | – |
| quantized | – | – | – |
| scale_factor | number | – | – |
| storage_bytes_float32 | – | – | – |
| storage_bytes_quantized | number | – | – |
{"vector":[0.1,-0.5,0.9,0]}vector_similarity ~86
Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scoring, embedding evaluation, and nearest-neighbor testing.
| Name | Type | Req | Description |
|---|---|---|---|
| metric | string | – | Distance metric (default: all) |
| vector_a | array | yes | First vector as array of floats |
| vector_b | array | yes | Second vector as array of floats |
| Name | Type | Req | Description |
|---|---|---|---|
| cosine_distance | – | – | – |
| cosine_similarity | – | – | – |
| dimension | – | – | – |
| dot_product | – | – | – |
| euclidean_distance | – | – | – |
| interpretation | – | – | – |
| manhattan_distance | – | – | – |
| norm_a | – | – | – |
| norm_b | – | – | – |
{"vector_a":[1,0],"vector_b":[0,1]}vector_stats ~121
Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.
| Name | Type | Req | Description |
|---|---|---|---|
| matrix | array | – | Matrix of vectors (overrides vector). Returns per-vector + matrix-level stats. Required unless `vector` is given. |
| top_k | number | – | Return indices of top K absolute values (default: 5) |
| vector | array | – | Single vector to analyze. Required unless `matrix` is given. |
| Name | Type | Req | Description |
|---|---|---|---|
| dimension | – | – | – |
| l2_norm | number | – | – |
| matrix_shape | array | – | – |
| matrix_stats | object | – | – |
| max | number | – | – |
| mean | number | – | – |
| min | number | – | – |
| per_vector | – | – | – |
| sparsity | number | – | – |
| std | number | – | – |
| top_k_indices | – | – | – |
{"vector":[1,2,3,4]}web_security_audit ~202
Run a comprehensive web security audit combining headers, SSL, CORS, and cookies checks — then use an LLM to produce a prioritised remediation plan. Orchestrates security_headers_check + ssl_certificate_check + cors_test + cookie_security_audit in parallel, merges all findings, then asks an AI model to: (1) rank vulnerabilities by real-world exploitability, (2) generate a remediation roadmap, (3) produce fix code snippets for the detected stack. Returns both raw audit data and the AI analysis. Use this as a one-click security posture assessment.
| Name | Type | Req | Description |
|---|---|---|---|
| api_key | string | – | Your Groq or HuggingFace API key. Required to enable AI analysis. |
| model | string | – | LLM model for AI analysis (default: "openai/gpt-oss-20b"). Set to "none" to skip AI analysis. |
| url | string | yes | Full URL to audit (e.g. https://example.com) |
| Name | Type | Req | Description |
|---|---|---|---|
| cookies | array | – | – |
| cookies_found | number | – | – |
| details | – | – | – |
| fix | – | – | – |
| grade | – | – | – |
| header | – | – | – |
| headers_checked | number | – | – |
| httpOnly | – | – | – |
| issues | – | – | – |
| key | – | – | – |
| message | string | – | – |
| missing | – | – | – |
| missing_count | number | – | – |
| name | – | – | – |
| origins_tested | number | – | – |
| overall_grade | – | – | – |
| risk_level | – | – | – |
| sameSite | – | – | – |
| score | – | – | – |
| secure | – | – | – |
| tests | – | – | – |
| total_findings | number | – | – |
| url | – | – | – |
| value | – | – | – |
| weak | – | – | – |
| weak_count | number | – | – |
| weight | – | – | – |
{"model":"none","url":"https://www.ia-qa.com"}webhook_endpoint_create ~105
Create a temporary webhook endpoint that captures incoming HTTP requests for one hour. Returns the webhook id, public URL, expiration timestamp, and current request count. Use together with webhook_endpoint_requests to inspect captured payloads.
| Name | Type | Req | Description |
|---|---|---|---|
| base_url | string | – | Optional public base URL. Default: https://www.ia-qa.com/mcp/webhook (the apex ia-qa.com answers 301 and a redirected POST loses its body, so an apex base_url is normalized to www) |
| Name | Type | Req | Description |
|---|---|---|---|
| expires_at | string | – | – |
| id | – | – | – |
| request_count | number | – | – |
| retention_minutes | number | – | – |
| url | string | – | – |
{}webhook_endpoint_requests ~78
Fetch the requests captured by a webhook created with webhook_endpoint_create. Returns the newest requests first with method, headers, query params, body payload, and timestamps.
| Name | Type | Req | Description |
|---|---|---|---|
| id | string | yes | Webhook id returned by webhook_endpoint_create |
| limit | number | – | Maximum number of requests to return (1-100, default: 20) |
| Name | Type | Req | Description |
|---|---|---|---|
| expires_at | string | – | – |
| id | string | – | – |
| request_count | number | – | – |
| requests | array | – | – |
{"id":"\u003cid returned by webhook_endpoint_create\u003e"}word_frequency ~111
Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extraction, and LLM output analysis.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | Text to analyze |
| min_length | number | – | Minimum word length to include (default: 3) |
| remove_stopwords | boolean | – | Remove common English stopwords (default: true) |
| top_n | number | – | Return top N words (default: 20, max: 200) |
| Name | Type | Req | Description |
|---|---|---|---|
| stopwords_removed | – | – | – |
| top_words | array | – | – |
| total_words | – | – | – |
| unique_words | number | – | – |
{"input":"the cat and the hat and the bat","remove_stopwords":false,"top_n":1}xml_to_json ~108
Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attributes.
| Name | Type | Req | Description |
|---|---|---|---|
| attr_prefix | string | – | Prefix for attribute keys (default: "@_") |
| ignore_attrs | boolean | – | Ignore XML attributes (default: false) |
| input | string | yes | XML string to convert |
| parse_values | boolean | – | Auto-parse numbers and booleans (default: true) |
| Name | Type | Req | Description |
|---|---|---|---|
| key_count | number | – | – |
| result | – | – | – |
{"input":"\u003cr a=\"1\"\u003e\u003ci\u003ex\u003c/i\u003e\u003ci\u003ey\u003c/i\u003e\u003c/r\u003e"}yaml_to_json ~102
Parse a YAML string and return the equivalent JSON value. The reverse of json_to_yaml. Supports nested objects, arrays, anchors, aliases, multi-document streams, and all scalar types. Use when processing config files, CI/CD pipeline definitions, or OpenAPI specs authored in YAML.
| Name | Type | Req | Description |
|---|---|---|---|
| input | string | yes | YAML string to parse |
| multi | boolean | – | If true, parse all documents in a multi-document stream and return an array (default: false) |
| Name | Type | Req | Description |
|---|---|---|---|
| count | number | – | – |
| documents | – | – | – |
| json | – | – | – |
{"input":"a: 1\nb: [x, y]"}How do I install the IA-QA — 152 QA & Dev Tools for AI Agents MCP server?
IA-QA — 152 QA & Dev Tools for AI Agents is a hosted endpoint at https://www.ia-qa.com/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · www.ia-qa.com
claude mcp add --transport http jcjamet-ia-qa-toolbox 'https://www.ia-qa.com/mcp'
What is the IA-QA — 152 QA & Dev Tools for AI Agents MCP server?
IA-QA — 152 QA & Dev Tools for AI Agents is an MCP server listed in the public MCP registry as io.github.JcJamet/ia-qa-toolbox. 152 free QA tools for agents: injection scan, evals, RAG, web audits. Smaller packs: /mcp/{pack}. This page covers its hosted endpoint (https://www.ia-qa.com/mcp).
Is the IA-QA — 152 QA & Dev Tools for AI Agents MCP server safe to use?
IA-QA — 152 QA & Dev Tools for AI Agents scores 89 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the IA-QA — 152 QA & Dev Tools for AI Agents MCP server expose?
IA-QA — 152 QA & Dev Tools for AI Agents exposes 152 tools: get_testing_guidelines, find_tool, generate_test_cases, jira_to_test_suite, analyze_diff_bugs, and 147 more. Their descriptions and schemas cost roughly 22,457 tokens of context every time the server is loaded.
Does the IA-QA — 152 QA & Dev Tools for AI Agents MCP server require authentication?
No. We connected to IA-QA — 152 QA & Dev Tools for AI Agents without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the IA-QA — 152 QA & Dev Tools for AI Agents MCP server still maintained?
IA-QA — 152 QA & Dev Tools for AI Agents is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What did VerifyMCP test on the IA-QA — 152 QA & Dev Tools for AI Agents MCP server?
VerifyMCP ran 13 checks on IA-QA — 152 QA & Dev Tools for AI Agents, covering TLS certificate, Authorization, HTTPS, HSTS header and 9 more. Supply Chain Security and Provenance & Transparency do not apply to a hosted endpoint, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.