VerifierMCP

An MCP server that gives any LLM the power to check its own work. Instead of trusting a model's arithmetic, regex, JSON or "the code prints X", the model can call a tool and verify it. Five verification tools over the Model Context Protocol: symbolic math, sandboxed code execution, JSON-Schema validation, dimensional analysis, and regex testing.

Built with the official MCP Python SDK (FastMCP). Works in Claude Desktop and any MCP client over stdio, or remotely over streamable HTTP (e.g. behind Amazon Bedrock AgentCore Gateway).

Why it matters

LLMs are confidently wrong about exactly the things that are mechanically checkable: (x+1)² = x²+1, a regex that doesn't quite match, JSON that violates its schema, a unit slip, "this function returns 5050". VerifierMCP turns each of those into a tool call, so an agent can replace an assertion with a verified fact.

Related MCP server: mcp-toolkit

The tools

Each tool body lives in src/verifier_mcp/tools/ as a pure function, so it is unit-tested without the protocol and reused by the web playground.

Quickstart

pip install -r requirements.txt

# Run the MCP server over stdio (what Claude Desktop launches)
PYTHONPATH=src python -m verifier_mcp.server

# Or serve it over HTTP (MCP streamable transport on :8000/mcp)
PYTHONPATH=src python -m verifier_mcp.server --http

# See it in action — the visual playground
PYTHONPATH=src uvicorn verifier_mcp.playground:app --port 8080   # http://localhost:8080

Connect to Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "verifier": {
      "command": "python",
      "args": ["-m", "verifier_mcp.server"],
      "env": { "PYTHONPATH": "/absolute/path/to/verifier-mcp/src" }
    }
  }
}

Restart Claude Desktop and ask it something like "Is (x+1)² equal to x²+1? Use the verifier." — it will call verify_math and come back with the checked answer.

Self-checking agent demo (Groq + MCP)

examples/self_checking_agent.py connects a Groq LLM to this server over real MCP (via langchain-mcp-adapters), instructs it to verify before asserting, and watches it catch its own mistake:

pip install -r requirements-demo.txt
export GROQ_API_KEY=gsk_...                       # real Groq agent
python examples/self_checking_agent.py

python examples/self_checking_agent.py --offline  # no key: scripted LLM, REAL MCP verification

[offline] Question: Is (x+1)^2 equal to x^2 + 1? If not, what is the correct expansion?

  [Assistant] draft answer: "(x+1)^2 = x**2 + 1"
  → verify_math((x+1)**2, claimed_result=x**2 + 1)  [real MCP call]
    MCP result: verified=False  —  expression simplifies to (x + 1)**2, not x**2 + 1
  → verify_math((x+1)**2, claimed_result=x**2 + 2*x + 1)  [real MCP call]
    MCP result: verified=True

  [Assistant] Corrected: (x+1)^2 = x^2 + 2x + 1 — verified, not x^2 + 1.

Deploy remotely (Amazon Bedrock AgentCore Gateway)

The HTTP transport (--http, container in Dockerfile) exposes a standard MCP endpoint at /mcp. Point AgentCore Gateway at it to turn these verifiers into tools any Bedrock agent can call:

docker build -t verifier-mcp .
docker run -p 8000:8000 verifier-mcp          # MCP over HTTP at http://localhost:8000/mcp

The sandbox

run_code executes snippets in a fresh python -I -S subprocess with no inherited environment, a temp working dir, and POSIX resource limits (CPU, memory, no forking, no file writes) plus a wall-clock timeout. That is appropriate for a local dev tool. For untrusted, multi-tenant use, run the whole server inside a container / gVisor / Firecracker — the subprocess limits are defense-in-depth, not a complete jail.

Tests

PYTHONPATH=src pytest -q      # 18 passed — tool logic + MCP protocol registration/dispatch

Tech stack

Model Context Protocol (FastMCP) · SymPy · jsonschema · Pint · FastAPI · Docker · GitHub Actions · Amazon Bedrock AgentCore Gateway–ready

License

MIT © Arash Omranpour