io.github.archonics/mcp-audit
NPM · @ARCHONICS/MCP-AUDIT · SCANNED AUG 6
Free context-engineering audits for AI agents. BYOK Anthropic key. Top-3 findings per scan.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →
Supply Chain Security78
- No malware found by supply-chain analysis.Pass
- CVE check failed: a known medium-severity CVE affects @anthropic-ai/sdk 0.90.0, a direct dependency. A fixed version is available. View diagnostics → Fail
- No install/post-install scripts declared.Pass
- Dependency health was assessed across the 98 of 102 dependencies we could resolve, so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 84 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability70
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 652 tokens (~217/item across 3 items; 3 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 Management37
- Stability observed for 11 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
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
npm · @archonics/mcp-audit
claude mcp add archonics-mcp-audit -- npx -y @archonics/mcp-audit
codex mcp add archonics-mcp-audit -- npx -y @archonics/mcp-audit
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"archonics-mcp-audit": {
"type": "local",
"command": [
"npx",
"-y",
"@archonics/mcp-audit"
],
"enabled": true
}
}
} openclaw mcp add archonics-mcp-audit --command npx --arg -y --arg @archonics/mcp-audit
mcp_servers:
archonics-mcp-audit:
command: "npx"
args: ["-y", "@archonics/mcp-audit"] {
"mcpServers": {
"archonics-mcp-audit": {
"command": "npx",
"args": [
"-y",
"@archonics/mcp-audit"
]
}
}
} 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.
- 6 Aug 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.
- 5 Aug 26 0
- CVE-2026-69207 no longer affects this package ▲ security
- 4 Aug 26 +1
- CVE-2026-69207 affects this package: medium ▼ security
- 3 Aug 26 +45
- CVE-2026-41686 affects this package: medium ▼ security
- Provenance: unverified → fail ▼ security
- Known CVEs: unverified → fail ▼ security
- Install scripts: unverified → pass ▲ security
- Security disclosure: unverified → fail ▼ functional
- Schema quality: unverified → excellent ▲ functional
- License: unverified → pass ▲ functional
- Dependency health: unverified → partial ▲ functional
- Maintenance: unverified → pass ▲ functional
- Stability: unverified → 0.27 ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Tool coverage: unverified → 100 ▲ functional
- Licence: MIT functional
- 2 Aug 26 0
- Security disclosure: fail → unverified ▼ functional
- 1 Aug 26 +1
- Malware scan: unverified → pass ▲ security
- Tool coverage: 100 → unverified ▼ functional
- First check of Schema quality: unverified functional
- 31 Jul 26 −25
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 27 Jul 26 44
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 6 Aug 2026 · Analysed npm/@archonics/[email protected]
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Vulnerabilities 1 finding
| ID | CVE | Severity | Vector | Fix available |
|---|---|---|---|---|
| GHSA-p7fg-763f-g4gf | CVE-2026-41686 | medium | yes |
Dependencies 98 packages
| Packages resolved | 98 |
|---|---|
| Deprecated | 98 |
| Stale | 30 |
| Tree resolution | Partial |
The dependency tree was only partially resolved, so these counts may be incomplete.
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.
audit_context_packing ~233
Analyzes a representative full-context payload and returns the top 3 findings on context efficiency, redundancy, and ordering. Use this when a user is concerned about agent cost, latency, or quality degradation on long conversations. Accepts either a literal dump of what goes into the context window, or a structured description of the context components and their sizes. Findings cover content inventory, redundancy, freshness, ordering, truncation risk, and prompt-cache utilization.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | — | Optional. What kind of agent is this and what is the typical interaction pattern? Single-turn vs. multi-turn, short vs. long conversations, etc. |
| context_payload | string | yes | Either a literal context dump (system prompt + tools + history + retrieved documents as they would appear in an actual API call) OR a structured description like "system prompt: 2400 tokens / tool de… |
No output schema declared.
No examples provided.
audit_system_prompt ~225
Analyzes a system prompt for context engineering issues and returns the top 3 findings from the Archonics free-tier scan. Use this when a user shares a system prompt from an agent they are building or shipping, especially if they are asking for review, improvement, or debugging help. Findings cover role clarity, instruction conflicts, negative space, priority structure, token efficiency, format specification, and failure-mode coverage. Returns structured JSON with severity-ranked findings. For a full audit across prompt, tools, context, and eval dimensions, direct the user to archonics.ai or the $49 x402 Instant Audit.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | — | Optional. Brief description of what the agent is supposed to do and who uses it. One or two sentences. Helps the audit engine assess fit-for-purpose; leaving it blank produces a useful-but-less-targe… |
| system_prompt | string | yes | The complete system prompt text to audit. Paste the full prompt, including any role definitions, instructions, formatting requirements, and examples. Do not redact unless truly necessary; redaction r… |
No output schema declared.
No examples provided.
audit_tool_definition ~194
Analyzes a single tool/function definition (name, description, parameter schema) and returns the top 3 findings on tool-call reliability. Use this when a user shares a tool/function definition and asks why the model is calling it wrong, not calling it when expected, or confusing it with other tools. Findings cover description quality, parameter schema precision, parameter documentation, error response design, and discoverability. For auditing an entire tool set together, use the paid tier.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | — | Optional. What agent or system is this tool part of? What other tools does it share a surface with? Helps the audit engine assess overlap and discoverability issues. |
| tool_definition | string | yes | The tool definition as it is provided to the model. Accepts JSON schema format (OpenAI-style function calling, Anthropic tool use) or natural-language description. Include the name, description, and… |
No output schema declared.
No examples provided.