Agent Data Tools: AI
Owner verifiedREMOTE · AGENT-DATA-TOOLS.REVMESH2074.WORKERS.DEV
15 tools: LLM chat, summaries, translation, embeddings, speech, images. From $0.002.
Available components
Last scanned (14 hours ago)
Scores higher than 57% of remote servers
Reachable. Last reached 23 Oct 2026.
Category AI and Machine Learning
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 Security69
- 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. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one. See how to fix → View diagnostics → Partial
- HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement. View diagnostics → Unverified
- The HSTS (Strict-Transport-Security) header is present. View diagnostics → Pass
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability68
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 2835 tokens (~189/item across 15 items; 15 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management13
- Stability observed for 4 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety75
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- 0 of 2 tool(s) whose name or description implies an irreversible operation declare an MCP destructiveHint annotation; "ai_chat" implies "send" and declares readOnlyHint instead, contradicting what its own name says it does. See how to fix → Fail
- An AI judge read all 16 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
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.
- 23 Oct 26 0
- Tool “ai_chat” rewrote its description, which is the text the model reads security
- Server version: 1.51.3 → 1.52.0 functional
- Server version: 1.50.5 → 1.51.3 functional
- 22 Oct 26 +1
- The server rewrote its instructions, which are the text every model session reads security
- Tool “ai_chat” rewrote its description, which is the text the model reads security
- Tool “ai_classify” rewrote its description, which is the text the model reads security
- Tool “ai_embed” rewrote its description, which is the text the model reads security
- Tool “ai_extract_json” rewrote its description, which is the text the model reads security
- Tool “ai_keywords” rewrote its description, which is the text the model reads security
- Tool “ai_outline” rewrote its description, which is the text the model reads security
- Tool “ai_rewrite” rewrote its description, which is the text the model reads security
- Tool “ai_sentiment” rewrote its description, which is the text the model reads security
- Tool “ai_speech” rewrote its description, which is the text the model reads security
- Tool “ai_summarize_text” rewrote its description, which is the text the model reads security
- Tool “ai_translate” rewrote its description, which is the text the model reads security
- Server version: 1.50.3 → 1.50.5 functional
- Server version: 1.50.2 → 1.50.3 functional
- Server version: 1.50.1 → 1.50.2 functional
- Server version: 1.49.1 → 1.50.1 functional
- Server version: 1.48.6 → 1.49.1 functional
- Server version: 1.48.4 → 1.48.6 functional
- Server version: 1.48.2 → 1.48.4 functional
- Server version: 1.48.0 → 1.48.2 functional
- Server version: 1.47.15 → 1.48.0 functional
- Server version: 1.47.7 → 1.47.15 functional
- Server version: 1.45.3 → 1.47.7 functional
- Tool “ai_classify” changed its title: Classify text into your labels → Text classification into labels cosmetic
- Tool “ai_extract_json” changed its title: Extract fields from text as JSON → Extract JSON fields from text cosmetic
- Tool “ai_keywords” changed its title: Extract keywords → Keyword extraction cosmetic
- Tool “ai_chat” changed its title: LLM chat completion: Llama 3.1 8B → LLM chat completions: Llama 3.1 8B cosmetic
- Tool “ai_embed” changed its title: Text embeddings (BGE-M3, 1,024 dimensions) → Embeddings, OpenAI compatible: BGE-M3, 1,024 dimensions cosmetic
- Tool “ai_chat” changed its title: Chat completion (Llama 3.1 8B) → LLM chat completion: Llama 3.1 8B cosmetic
- 21 Oct 26 0
- Tool “ai_chat” rewrote its description, which is the text the model reads security
- Server version: 1.45.1 → 1.45.3 functional
- Server version: 1.45.0 → 1.45.1 functional
- Server version: 1.44.0 → 1.45.0 functional
- Tool “ai_embed” changed its title: Text embeddings (BGE-M3) → Text embeddings (BGE-M3, 1,024 dimensions) cosmetic
- Tool “ai_sentiment” changed its title: Sentiment of text → Sentiment analysis of text cosmetic
- Tool “ai_speech” changed its title: Text to speech (audio file) → Text to speech (TTS): turn text into an audio file cosmetic
- Tool “image” changed its title: Generate an image from a text prompt → Image generation from a text prompt cosmetic
- Tool “transcribe” changed its title: Speech to text for an audio file → Transcribe audio: speech to text for an audio file cosmetic
- 20 Oct 26 0
- The server rewrote its instructions, which are the text every model session reads security
- Tool “ai_chat” rewrote its description, which is the text the model reads security
- Tool “ai_chat_large” rewrote its description, which is the text the model reads security
- Tool “ai_outline” rewrote its description, which is the text the model reads security
- Tool “ai_rewrite” rewrote its description, which is the text the model reads security
- Tool “ai_speech” rewrote its description, which is the text the model reads security
- Tool “ai_translate” rewrote its description, which is the text the model reads security
- Tool “image” rewrote its description, which is the text the model reads security
- Schema quality: 2409 → 2829 ▼ functional
- Stability: unverified → 0.03 ▲ functional
- Server version: 1.41.0 → 1.44.0 functional
- Server version: 1.40.0 → 1.41.0 functional
- Server version: 1.39.3 → 1.40.0 functional
- Server version: 1.37.1 → 1.39.3 functional
- Server version: 1.36.1 → 1.37.1 functional
- Server version: 1.35.3 → 1.36.1 functional
- New tool “ai_chat_large” functional
- 19 Oct 26 67
First indexed and scored.
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 23 Oct 2026 · Probed https://agent-data-tools.revmesh2074.workers.dev/mcp/ai
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=revmesh2074.workers.dev | CN=YE1,O=Let's Encrypt,C=US | 9 Oct 2026 | 7 Jan 2027 | ECDSA 256 | ECDSA-SHA384 | 538c1f22b424c14420d956229d7db3228c3 |
| SANs: *.revmesh2074.workers.dev, revmesh2074.workers.dev | ||||||
| CN=YE1,O=Let's Encrypt,C=US (CA) | CN=Root YE,O=ISRG,C=US | 3 Sept 2025 | 2 Sept 2028 | ECDSA 384 | ECDSA-SHA384 | 5ddd70dd31f801c85c186a7a04b80afe |
| CN=Root YE,O=ISRG,C=US (CA) | CN=ISRG Root X2,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | ECDSA-SHA384 | 872165fc34b6e5fba8add5b3705fb53a |
| CN=ISRG Root X2,O=Internet Security Research Group,C=US (CA) | CN=ISRG Root X1,O=Internet Security Research Group,C=US | 13 May 2026 | 2 Sept 2032 | ECDSA 384 | SHA256-RSA | 6c8f1dc727c7117f7baf853ac980f9cd |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of agent-data-tools.revmesh2074.workers.dev. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| dev. | present | 60074 | 8 | Verified |
| workers.dev. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
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=31536000 |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://agent-data-tools.revmesh2074.workers.dev/mcp/ai | Verified | 200 | |
| http (plaintext) | http://agent-data-tools.revmesh2074.workers.dev/mcp/ai | Inconclusive | 405 |
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 →
ai_chat LLM chat completions: Llama 3.1 8B ~203
Send a prompt, get an answer from Llama 3.1 8B Instruct, with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Use for drafting, reasoning steps, Q&A and any quick LLM call without an AI provider account. For harder reasoning use ai_chat_large. Example: prompt=Explain x402 in one sentence.. Price $0.002/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| maxTokens | integer | – | Maximum output tokens, 1-1024 (default 400) |
| prompt | string | yes | User prompt, up to 8,000 characters |
| system | string | – | Optional system prompt, up to 2,000 characters |
| temperature | number | – | 0-1.5 (default 0.6) |
No output schema declared.
No examples provided.
ai_chat_large Chat completion (Llama 3.3 70B or gpt-oss-120b) ~248
Send a prompt, get an answer from a larger open model: Llama 3.3 70B Instruct (default) or OpenAI gpt-oss-120b, with an optional system prompt, up to 1,024 output tokens and adjustable temperature. Same call as /ai/chat with a stronger model. Use when the 8B model is not enough: reasoning, drafting and Q&A. Example: prompt=Explain x402 in one sentence.. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| maxTokens | integer | – | Maximum output tokens, 1-1024 (default 400) |
| model | string | – | llama-3.3-70b (default) or gpt-oss-120b |
| prompt | string | yes | User prompt, up to 8,000 characters |
| system | string | – | Optional system prompt, up to 2,000 characters |
| temperature | number | – | 0-1.5 (default 0.6) |
No output schema declared.
No examples provided.
ai_classify Text classification into labels ~125
Zero-shot classification: give text and 2-20 candidate labels, get the best label, a short reason and a ranking. Use for routing tickets, tagging content, intent detection and moderation queues. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| labels | string | yes | Comma-separated candidate labels (2-20) |
| text | string | yes | Text to classify, up to 8,000 characters |
No output schema declared.
No examples provided.
ai_embed Embeddings, OpenAI compatible: BGE-M3, 1,024 dimensions ~130
A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication. Example: text=pay-per-call APIs for AI agents. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | Text to embed, up to 4,000 characters |
No output schema declared.
No examples provided.
ai_extract_json Extract JSON fields from text ~131
Pull named fields out of unstructured text (emails, invoices, listings, bios) and get them back as a JSON object, with null for anything not present. Use to turn messy text into structured data for an agent. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| fields | string | yes | Comma-separated field names to extract (1-30) |
| text | string | yes | Source text, up to 12,000 characters |
No output schema declared.
No examples provided.
ai_keywords Keyword extraction ~111
The most important keywords and key phrases of a text, most relevant first. Use for tagging, SEO, search indexing and topic detection. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| count | integer | – | How many, 3-30 (default 10) |
| text | string | yes | Text, up to 12,000 characters |
No output schema declared.
No examples provided.
ai_outline Outline a text or topic ~139
A structured Markdown outline (headings and bullet points) of a text you send, or a suggested outline for a topic. Use to plan articles, reports, talks and documentation. Example: text=How small businesses can accept stablecoin payments. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| depth | integer | – | Heading levels, 1-3 (default 2) |
| text | string | yes | Text to outline, or a topic, up to 12,000 characters |
No output schema declared.
No examples provided.
ai_rewrite Rewrite text in a style ~149
Rewrite text to be more formal, casual, simpler, shorter, friendlier, more professional or more persuasive, keeping the meaning and language. Use to polish emails, replies, product copy and messages. Example: text=hey, cant make it tmrw, can we move to thu?. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| style | string | – | formal, casual, simpler, shorter, friendly, professional or persuasive (default professional) |
| text | string | yes | Text, up to 8,000 characters |
No output schema declared.
No examples provided.
ai_sentiment Sentiment analysis of text ~123
Positive or negative sentiment of English text with a confidence score, from a DistilBERT model fine-tuned on SST-2. Use to triage reviews, support messages, social posts and replies. Example: text=The checkout was fast and support answered within minutes.. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| text | string | yes | English text, up to 4,000 characters |
No output schema declared.
No examples provided.
ai_speech Text to speech (TTS): turn text into an audio file ~163
Turn up to 1,000 characters of text into natural speech as an audio file (base64, with its content type), in English, Spanish, French, Chinese, Japanese or Korean, with MeloTTS. Use for voice replies, audio notes, accessibility and phone or chat bots. Example: text=Your payment was received. Thank you!. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| lang | string | – | Language: en, es, fr, zh, ja or ko (default en) |
| text | string | yes | Text to speak, up to 1,000 characters |
No output schema declared.
No examples provided.
ai_summarize_text Summarize text ~139
Summarize up to 12,000 characters of text you send into a few sentences plus bullet key points. Use for long emails, documents, transcripts and tool outputs (for a web page by URL, use /summarize). Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| sentences | integer | – | Summary length in sentences, 1-8 (default 3) |
| text | string | yes | Text to summarize, up to 12,000 characters |
No output schema declared.
No examples provided.
ai_translate Translate text (100 languages) ~167
Translate text between about 100 languages with Meta's M2M100 1.2B model. Give the target language as an ISO code such as en, sv, de, fr, es, zh or ja; the source language is optional. Use to localise messages and documents. Example: text=Payment received, thank you!, to=sv. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| from | string | – | Source language code (default en) |
| text | string | yes | Text, up to 4,000 characters |
| to | string | yes | Target language code, e.g. sv |
No output schema declared.
No examples provided.
describe Describe or question an image ~164
Vision model for an image at a public URL: get a detailed description including any visible text, or pass a question to ask something specific about the image. Accepts PNG, JPEG, WebP and GIF up to 3 MB. Use for alt text, OCR-style reading of screenshots, chart reading and content checks. Price $0.01/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| question | string | – | Optional question about the image (max 300 characters); default is a detailed description |
| url | string | yes | Absolute http(s) URL of a PNG, JPEG, WebP or GIF image (max 3 MB) |
No output schema declared.
No examples provided.
image Image generation from a text prompt ~163
Text-to-image generation with FLUX.1 schnell. Send a prompt, get a 1024x1024 JPEG back as base64 in a few seconds. Use for illustrations, thumbnails, mockups, avatars and social media images. Optional steps parameter (1-8, default 4) trades speed for detail. Example: prompt=a lighthouse on a cliff at sunset, watercolor. Price $0.02/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| prompt | string | yes | What the image should show (3-800 characters) |
| steps | integer | – | Diffusion steps, 1-8 (default 4) |
No output schema declared.
No examples provided.
transcribe Transcribe audio: speech to text for an audio file ~163
Transcribe speech from an audio file at a public URL using Whisper large-v3-turbo. Returns the transcript text, detected language, duration and word count. Accepts common formats such as MP3, WAV, M4A and OGG up to 5 MB. Use for voice notes, podcast clips, meeting snippets and voice messages. Example: url=https://commons.wikimedia.org/wiki/Special:FilePath/En-us-hello.ogg. Price $0.02/call via x402 USDC or a card key (/credits); failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.
| Name | Type | Req | Description |
|---|---|---|---|
| url | string | yes | Absolute http(s) URL of an audio file (max 5 MB) |
No output schema declared.
No examples provided.
How do I install the Agent Data Tools: AI MCP server?
Agent Data Tools: AI is a hosted endpoint at https://agent-data-tools.revmesh2074.workers.dev/mcp/ai, 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 · agent-data-tools.revmesh2074.workers.dev
claude mcp add --transport http nutshell2000-agent-data-tools-ai 'https://agent-data-tools.revmesh2074.workers.dev/mcp/ai'
What is the Agent Data Tools: AI MCP server?
Agent Data Tools: AI is an MCP server listed in the public MCP registry as io.github.Nutshell2000/agent-data-tools-ai. 15 tools: LLM chat, summaries, translation, embeddings, speech, images. From $0.002. This page covers its hosted endpoint (https://agent-data-tools.revmesh2074.workers.dev/mcp/ai). Its publisher has verified ownership with us.
Is the Agent Data Tools: AI MCP server safe to use?
Agent Data Tools: AI scores 68 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 Agent Data Tools: AI MCP server expose?
Agent Data Tools: AI exposes 15 tools: image, transcribe, describe, ai_chat, ai_chat_large, and 10 more. Their descriptions and schemas cost roughly 2,318 tokens of context every time the server is loaded.
Does the Agent Data Tools: AI MCP server require authentication?
No. We connected to Agent Data Tools: AI without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the Agent Data Tools: AI MCP server still maintained?
Agent Data Tools: AI is still listed as active in the MCP registry. We last reached this channel on 23 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.