mcp-audit

name	mcp-audit
description	Audit an MCP server for conformance — lint its tool schemas and behaviorally fuzz it (call tools with malformed input), then score it 0–100 (grade A–F) with the exact findings and fixes. Use when the user wants to check, test, validate, score, benchmark, or debug an MCP server, its tools or tool schemas, or figure out why an MCP server/tool behaves badly with an agent.

Audit an MCP server (MCProbe)

MCProbe scores an MCP server's conformance — how reliably an AI agent can actually use it — across schema quality and behavioral robustness. Reach for it whenever the user wants to evaluate, test, compare, or debug an MCP server.

Pick the path that's available

A. MCProbe is connected as an MCP server (you have probe_* tools). Run the four core tools in order:

1. probe_connect — dial the target: an HTTPS URL, or a stdio command.
2. probe_report — the one-shot audit: lints the schemas, fuzzes behavior, and returns the full report + 0–100 score. (Use probe_lint / probe_fuzz individually if you want just one phase.)
3. probe_disconnect when finished.

B. The mcprobe CLI is available (no MCP tools connected).

# Remote HTTP(S) server
npx mcprobe audit https://example.com/mcp --fuzz

# Local stdio server (the `npx some-server` style)
npx mcprobe audit --stdio "npx @acme/my-mcp-server" --fuzz

# Machine-readable output (for scripting / CI gates)
npx mcprobe audit https://example.com/mcp --fuzz --json

Omit --fuzz for a fast static (schema-only) audit. Add --json to parse the result programmatically.

What --fuzz does — and the safety rule

Fuzzing calls each tool with malformed input to test error handling and liveness. This is what catches the dangerous problems a linter can't: a tool that silently accepts garbage (so the agent trusts a wrong result) or one that crashes the session. It is dry-run by default — tools annotated destructiveHint: true are skipped unless you pass --fuzz-destructive, so a normal run is safe even on servers you don't own. Only fuzz servers you're allowed to test.

How to read the result

• Overall 0–100 + grade A–F (A ≥ 90, B ≥ 75, C ≥ 60, D ≥ 40, F < 40).
• Four dimensions, each 0–10: schema quality, error handling, liveness, and metadata/annotations.
• Critical-issues callout: the behaviors that hurt agents and users most, so surface them first — tools that silently accept bad input, tools that return an empty success on a valid call (a hallucinated success: the tool reports "done" but returns/persists nothing), tools that return a success that violates their declared outputSchema (the payload doesn't honor the contract the tool advertised), and protocol crashes.
• Findings carry stable dotted codes (tool.missing_description, param.untyped, schema.invalid, …) and fix hints. Report the worst-severity ones with the exact tool/parameter and the recommended fix.

Optional: save the run to a dashboard (needs an mcprobe.org Pro key)

push runs the same audit and uploads it to the user's mcprobe.org history and public gallery. The audit itself is always free and local; only the hosted upload is the Pro tier.

How the user gets a key (one-time):

1. Go to https://www.mcprobe.org and sign up (email + password, then confirm via the email link).
2. Go Pro — $9.90 once, lifetime. Click "Go Pro" / "Unlock" and pay by crypto (card checkout is coming soon).
3. Open My Profile → https://www.mcprobe.org/app/profile.
4. In the "Audit a local (stdio) server" card, click Generate key. A token like mcp_… appears — it's shown only once, so copy it right away.
5. Store it as an environment variable (keeps it out of shell history / code):

   export MCPROBE_TOKEN="mcp_your_key_here"
   

Then upload any audit:

npx mcprobe push https://example.com/mcp --fuzz --token "$MCPROBE_TOKEN"

The CLI also reads MCPROBE_TOKEN from the environment, so --token can be omitted once it's set.

More detail (scoring model, the 12 lint rules, the fuzz categories): https://github.com/alitiknazoglu/mcprobe