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mcp-audit

mcp-audit

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github.com/archonicsAI & Agents1Updated 24w ago

About mcp-audit

mcp-audit is an MCP server for auditing production AI agents’ prompts, tool definitions, and context packing. It returns three ranked findings for each scan, using an Anthropic API key supplied by the user.

Key features

  1. 01Audit system prompts
  2. 02Review tool definitions
  3. 03Analyze context packing
  4. 04Rank three findings by severity

Use cases

  1. 01Find conflicting system prompt instructions
  2. 02Improve tool-call reliability
  3. 03Identify redundant or stale context

MCP support

It exposes three audit tools to MCP clients, including Claude Desktop, Cursor, and Claude Code. The client connects using the user's Anthropic API key.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"archonics-mcp-audit": {
"command": "npx",
"args": [
"-y",
"@archonics/mcp-audit"
],
"env": {
"ANTHROPIC_API_KEY": "<ANTHROPIC_API_KEY>"
}
}
}
}
Run commandnpx -y @archonics/mcp-audit
Environment variables to set
ANTHROPIC_API_KEYSecretYour Anthropic API key. Required — the audit engine calls Claude directly from your machine.

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@archonics/mcp-audit0.1.4

Tools

3

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
contextstringNoOptional. 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-targeted audit.
system_promptstringYesThe 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 reduces audit quality.

Details

From the project README

Archonics MCP Audit Server

Free-tier context engineering audits for production AI agents, delivered as MCP tools you can call from Claude Desktop, Cursor, Claude Code, or any MCP-compatible client.

What you get: top-3 findings on your system prompts, tool definitions, or context packing, on demand, no account needed.

What it costs: nothing. The free scan is genuinely free. Upgrade paths to the $49 Instant Audit and $750 Full Audit are surfaced in the response footer; they're not paywalls on this tool.

Why this exists

Most production agent failures aren't model failures — they're context engineering failures. Ambiguous instructions, underspecified tools, bloated context, no regression tests on prompt changes. Those problems are spottable by a trained reader. Archonics has trained that reader and published it as an MCP tool so you can get a second opinion on your agent's context without filing a support ticket.

The underlying audit engine applies Archonics Audit Methodology v1.0, the same spec that drives our paid audits.

Tools

audit_system_prompt

Paste a system prompt. Get back the three most important context engineering issues in it, ranked by severity, with specific recommendations.

Covers: role clarity, instruction conflicts, negative space, priority structure when instructions conflict, token efficiency, format specification precision, failure-mode coverage.

audit_tool_definition

Paste a tool/function definition. Get back the three most important issues affecting how reliably the model will call it.

Covers: description quality (the "when to use this tool" question), parameter schema precision, parameter documentation, error response design, discoverability.

audit_context_packing

Paste a representative context payload (or describe it structurally). Get back the three most important efficiency and quality issues.

Covers: content inventory, redundancy across sections, freshness/relevance, ordering, truncation risk, prompt-cache utilization.

Installation

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "archonics-audit": {
      "command": "npx",
      "args": ["-y", "@archonics/mcp-audit"],
      "env": {
        "ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
      }
    }
  }
}
Cursor

Add to your .cursor/mcp.json:

{
  "mcpServers": {
    "archonics-audit": {
      "command": "npx",
      "args": ["-y", "@archonics/mcp-audit"],
      "env": {
        "ANTHROPIC_API_KEY": "your-anthropic-api-key-here"
      }
    }
  }
}
Claude Code
claude mcp add archonics-audit npx -y @archonics/mcp-audit

Then set ANTHROPIC_API_KEY in your environment.

Why does it need my Anthropic API key?

The audit engine runs on Claude. You bring your own API key so:

  1. Audit submissions go directly from your machine to Anthropic's API, never through Archonics servers.
  2. Your costs are transparent — a typical audit uses 2,000–4,000 tokens, well under a penny.
  3. There's no "free but actually limited" rate-limit surprise. Your API key, your limits.

If you'd rather not bring your own key, use the $49 Instant Audit at agent.market — we cover the API costs and return a full-methodology audit PDF.

Privacy

Submitted content is processed ephemerally. No prospect content is retained on Archonics infrastructure or used to train any model. The API call pattern is: your client → your Anthropic API key → Anthropic → your client. Archonics servers are not in this path.

Aggregated, anonymized patterns across many audits may inform improvements to the methodology — "18 of 20 audited systems lacked prompt-regression tests" — but specific content never feeds that process.

Details: archonics.ai/privacy

Upgrade paths

If the free scan surfaces issues worth fixing, two paid tiers go deeper:

  • Instant Audit — $49 USDC via x402. Full methodology applied programmatically to a system you submit. 5-10 page PDF report covering all four dimensions (prompt, tools, context, eval) rather than just three findings in one dimension. Listed at agent.market/archonics.
  • Full Audit — $750. Human-reviewed audit of a complete agent system. 15-25 page report tuned to your team's context. Contact [email protected].

Contact

License

MIT. Use it, fork it, audit yourself.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.