CheckMCP

Vendor-neutral quality / security / context-cost audit & score for any MCP server. One uvx/pipx command → an MCP Score /100 + causal opportunities (why the score), Lighthouse-style.

Installed from PyPI as audit-mcp (the name checkmcp was already taken); the command is audit-mcp. Brand, site and repo remain CheckMCP / checkmcp.dev.

uvx audit-mcp https://mcp.deepwiki.com/mcp
# or
pipx run audit-mcp https://mcp.context7.com/mcp --json
audit-mcp https://my-mcp.example.com/mcp --token "$TOKEN"

Use it as an MCP server

audit-mcp mcp turns the auditor itself into an MCP server (stdio) exposing one tool, audit_mcp_server — so your agent can answer "is this MCP server safe?" mid-conversation.

# Claude Code
claude mcp add audit-mcp -- uvx audit-mcp mcp

// Cursor (.cursor/mcp.json) / Claude Desktop (claude_desktop_config.json)
{ "mcpServers": { "audit-mcp": { "command": "uvx", "args": ["audit-mcp", "mcp"] } } }

Registry name: io.github.H129hj/checkmcp

No dependencies (stdlib only). tiktoken optional for exact token counts.

What it measures (7 pillars)

• Security — OWASP MCP Top 10 (tool poisoning, hardcoded secrets, command injection), lethal-trifecta.
• Tool design — sprawl/consolidation (percentile-calibrated on real servers: median ~7 tools, p95 ~42).
• Schemas / desc — descriptions + inputSchema/outputSchema completeness.
• Context-cost — tokens spent on tools/list, paid on every request (the #1 pain of 2026).
• Compliance — protocol-version gap, annotations, JSON-RPC error conformance, OAuth discovery.
• Reliability — single-shot today (not credited; continuous T3 monitoring on checkmcp.dev).
• Coverage — the 3 primitives (tools + resources + prompts).

Hard floors: secret-in-schema → cap D, failed handshake → cap F. Every penalty is attributed: measure → mechanism → effect → Δscore.

CLI flags

flag	what
--json	machine-readable report
--badge	SVG badge + README embed snippets
--html	standalone SEO/GEO page (JSON-LD SoftwareApplication + FAQ)
--repo owner/name	add maintenance/license/provenance signal from GitHub
--token <bearer>	audit an OAuth-protected server
--min-score N	CI: exit 1 if MCP Score < N
--baseline file	CI: pin tool definitions; fail on regression (rug-pull)
--gh-summary	CI: write a Markdown summary to $GITHUB_STEP_SUMMARY
--deep	runtime depth via an external scanner (mcp-scan/snyk) if present
--evals	behavioral sandbox: actually invokes read-only tools with canary inputs to catch tool-output prompt-injection, exfiltration vectors, secret/PII leakage and context bombs (sends real traffic; CI-fails on a malicious verdict)

GitHub Action

# .github/workflows/mcp-audit.yml
name: MCP audit
on: [push, pull_request]
jobs:
  checkmcp:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: H129hj/checkmcp@v1
        with:
          url: https://my-mcp.example.com/mcp
          min-score: "70"
          baseline: .checkmcp-baseline.json   # commit it → fails on rug-pull

Behavioral evals (--evals)

Static analysis catches declared danger; --evals catches runtime danger by actually invoking read-only tools with canary inputs and inspecting the responses for tool-output prompt-injection, exfiltration vectors and secret/PII leakage (multilingual; optional callback-canary confirms exfil). CI-fails on a malicious verdict.

Self-hosted security gateway

Beyond auditing, CheckMCP ships an in-band MCP gateway — a proxy you put between your agent and an MCP server. It inspects every call, and in active mode blocks/strips tool-poisoning & exfiltration before they reach the agent. Run it in your own infra (tool traffic never leaves your network):

docker pull ghcr.io/h129hj/checkmcp-gateway:latest   # or build from source
docker run -p 8080:8080 -e GATEWAY_BACKEND_URL=https://mcp.example.com/mcp \
  -e GATEWAY_MODE=active -e GATEWAY_SECRET=$(openssl rand -hex 16) \
  ghcr.io/h129hj/checkmcp-gateway:latest

See GATEWAY.md for config (passive/active, OAuth backends, policy, logs).

Hosted

Full reports, public directory, live badges, continuous drift monitoring, a governance policy API and a hosted gateway at checkmcp.dev.

Honest limitations

• Percentile bands come from a growing corpus (one+ registries) — widening over time.
• Exact tokens with pipx install "audit-mcp[exact-tokens]" (cl100k_base); otherwise chars/4 approximation.
• Pillar weights are expert priors. python -m checkmcp.calibrate samples.json validates them against a labeled agent-success sample (per-pillar correlation + OLS-suggested weights + construct-validity R²) — supply real outcomes to close the loop.

MIT.