mcp-checkup 0.6.0

🩺 A health check for your MCP setup — measure the context tax and hygiene of your MCP servers

🩺 mcp-checkup

A health check for your MCP setup.

One command that tells you what your MCP servers cost you — in context-window tokens and dollars — and whether they follow basic security hygiene.

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The problem

Every MCP server you connect injects its full tool schemas into your model's context window — on every single request — before you type a word.

• GitHub's official MCP server alone consumes ~17,600 tokens per request in tool definitions (autopsy).
• Teams have burned 72% of a 200k context window on tool definitions before doing any actual work (MCP spec issue #2808).
• A security audit of publicly exposed MCP servers found 119 out of 119 sampled allowed unauthenticated access to internal tool listings.

You are paying a context tax on every request, and you probably have no idea how big it is. mcp-checkup measures it.

What it looks like

Real output of a bare uvx mcp-checkup on a developer laptop (v0.2.0):

$ uvx mcp-checkup

  🩺 MCP Checkup — 5 server(s), 128 tools
  ┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
  ┃ Server        ┃ Clients        ┃ Tools ┃ anthropic ┃ openai ┃ gemini ┃
  ┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
  │ alpaca        │ claude-code    │    69 │    18,611 │ 19,025 │ 18,542 │
  │ edgartools    │ claude-code    │    13 │     3,725 │  3,803 │  3,712 │
  │ playwright    │ claude-desktop │    23 │     3,198 │  3,336 │  3,175 │
  │ filesystem    │ claude-desktop │    14 │     1,640 │  1,723 │  1,626 │
  │ yahoo-finance │ claude-code    │     9 │     1,396 │  1,450 │  1,387 │
  ├───────────────┼────────────────┼───────┼───────────┼────────┼────────┤
  │ Total         │                │   128 │    28,570 │ 29,337 │ 28,442 │
  └───────────────┴────────────────┴───────┴───────────┴────────┴────────┘
  Context tax: ~28,916 tokens on Anthropic models (incl. 346 tool-use
  system overhead), before your first message.

That's 14% of a 200k context window, spent before the first user message.

Supported clients (auto-discovery)

Client	Config discovered
Claude Desktop	claude_desktop_config.json (macOS/Windows/Linux)
Claude Code	project .mcp.json + ~/.claude.json user/local scopes
Cursor	~/.cursor/mcp.json + project .cursor/mcp.json
Windsurf	~/.codeium/windsurf/mcp_config.json
VS Code	.vscode/mcp.json + user-profile mcp.json (servers key)

Hygiene checks

Every scan also runs read-only hygiene checks over what your servers advertise — sample real findings:

  Hygiene findings (10)
  W01 medium alpaca > get_portfolio_history: tool is 8.7x its minimal schema (1443 vs 166 tokens)
  W01 medium alpaca > replace_order_by_id:   tool is 3.3x its minimal schema (1501 vs 451 tokens)
  W04 low    alpaca: server exposes 69 tools; models degrade with large tool lists
  W05 low    get_option_chain is advertised by 2 servers: alpaca, yahoo-finance

ID	Checks for
W01–W05	Schema bloat vs minimal equivalent, oversized descriptions, enum explosions, tool-count explosion, cross-server duplicate tools
H01	Remote HTTP servers configured without client credentials
H02	Tool-poisoning language in descriptions (hidden instructions, concealment, sensitive-path exfiltration)
H03	Cross-server tool shadowing ("instead of X, always…")
H04	Write/exec tools alongside network-fetch tools (exfiltration-chain heuristic)

All checks are heuristics — disable any with --disable-check ID; see details with --verbose. For deep security-only scanning also consider invariantlabs' mcp-scan; mcp-checkup's focus is the combined cost + hygiene physical.

Quickstart

uvx mcp-checkup          # or: pip install mcp-checkup

Point it at your client config (.mcp.json, Claude Desktop, Cursor, …) or a running server. That's the whole interface — one command, one report.

Roadmap

Full detail per milestone in ROADMAP.md.

Version	Theme	One line
v0.1.0	Weigh one server	weigh <target> prints a per-tool token table for one server
v0.2.0	Auto-discovery	Zero-flag scan of Claude Desktop/Code, Cursor, Windsurf, VS Code
v0.3.0	Dollars & context tax	$/request, $/session, and context-tax % per model
v0.4.0	Hygiene	Weight (W01–W05) and security (H01–H04) checks, two-pillar report
v0.5.0	CI gate	Budget/severity gates, stable exit codes, GitHub Action
v0.6.0	--fix emit	Semantic-safe schema compression with before/after report
v0.7.0	Trim proxy + rug-pull pins	serve --wrap --trim proxy, changed-since-pin detection
v0.8.0	Shareable reports	Self-contained HTML report, badge endpoint, diff command
v0.9.0	Hardening + SDK v2	MCP SDK v2 transport, Windows CI, perf budget
v1.0.0	Stable	Frozen CLI/exit codes/schema with a deprecation policy

Put your schemas on a diet

mcp-checkup fix <server> builds compressed versions of every tool schema — semantic-safe: tool names, types, and required fields never change; only prose (descriptions, titles, examples) and over-restrictive sugar are trimmed. Measured on real servers with the default policy (keep first sentence):

$ mcp-checkup fix "npx -y @modelcontextprotocol/server-everything"
  Total: 1,084 -> 735 tokens   % saved: 32.2%

$ mcp-checkup fix "python toy_bloated_server.py"
  search: 1,243 -> 336 tokens (73% saved)

• --emit DIR writes drop-in sidecar schema files + a TOOLS.md for the server's author
• --emit-pr-text FILE generates a polite, data-driven issue body to file upstream

Honest limit: your client still fetches the original schemas from the server — permanent fixes belong server-side, or use the trim proxy (v0.7).

Gate your context budget in CI

Treat context bloat like coverage: gate it. Exit codes are a stable contract — 0 ok, 1 token budget exceeded, 2 hygiene findings at/above threshold, 3 no server reachable.

# .github/workflows/mcp-checkup.yml
- uses: mohitgurnani1/mcp-checkup@v0
  with:
    fail-over-total: "20000"
    fail-on-severity: high

Or directly: uvx mcp-checkup scan --fail-over-total 20000 --fail-on-severity high. --format markdown emits a PR-comment-ready report; --write-baseline records a snapshot and later runs print per-server token drift against it.

How counting works (and its error bars)

Each tool schema is serialized into every provider's actual wire format (Anthropic tools, OpenAI functions, Gemini functionDeclarations) and counted with tiktoken's o200k_base encoding — exact for the GPT-4o/GPT-5 family, a close proxy (±~10%) for Anthropic and Gemini. Anthropic totals add the documented tool-use system-prompt overhead. For billing-exact Anthropic numbers, install mcp-checkup[precise], set ANTHROPIC_API_KEY, and pass --precise — it queries the free count_tokens API with your real schemas.

Prices and context windows come from a vendored snapshot of LiteLLM's community-maintained pricing table; --refresh-pricing fetches the latest at runtime (with silent fallback). $ per session assumes schemas are resent every turn — with prompt caching you pay full price on turn one and on every cache invalidation.

Status

Early days — the measurement engine is being built in the open. If the context tax bothers you too: ⭐ star the repo to follow along, open an issue with your worst MCP token bill, or see CONTRIBUTING.md to help build it.

License

Apache-2.0