mcp-test-bench

MCP Test Bench

Video walkthrough: https://youtu.be/rGaS_9dsOao 60-second overview: https://youtu.be/eqE1Nj6BDIk

Open-source evaluation harness for MCP servers: auto-discovers tools, runs LLM-judged scenarios, flags security issues, and shows traces in a web dashboard.

demo

What it is

MCP Test Bench is a local evaluation harness for Model Context Protocol servers. Point it at any MCP server — a stdio command or an SSE URL — and it discovers every tool, resource, and prompt the server exposes, then auto-generates realistic test scenarios from the schemas using Claude. It drives those scenarios through an agent loop, records every tool call and response, scores each run with an LLM-as-judge against configurable rubrics (correctness, safety, efficiency, hallucination), and audits the server for security issues such as prompt-injection patterns in tool descriptions, unbounded outputs, and PII leakage.

Everything is stored in a local SQLite file and visible in a Next.js dashboard: run history, score timelines, side-by-side server comparisons, and full trace views. A mcpbench CLI makes the whole pipeline scriptable for CI.

Quickstart

git clone https://github.com/RitikPatill/mcp-test-bench.git
cd mcp-test-bench
pnpm i
export ANTHROPIC_API_KEY=sk-ant-...
pnpm dev
# open http://localhost:3000

Requires Node >= 20 and pnpm 9.

Usage

Add a server through the dashboard by pasting a stdio command (e.g. npx -y @modelcontextprotocol/server-filesystem /tmp). Test Bench discovers its tools, auto-generates scenarios, and lets you kick off an eval with one click. The trace timeline streams live as Claude calls tools and the judge scores each turn. When it finishes, the report shows an overall score, per-rubric breakdowns, failed scenarios with reasoning, and any security findings.

For CI, build the CLI and run a config file directly:

pnpm --filter cli build
node apps/cli/dist/index.js run examples/demo/filesystem-server.yaml

Config files are plain YAML — see examples/demo/filesystem-server.yaml for the minimal shape.

Architecture

flowchart LR
    UI[Next.js Dashboard] -->|REST/SSE| API[API Routes]
    API --> Runner[Eval Runner]
    API --> DB[(SQLite)]
    Runner --> MCPClient[MCP Client\nstdio + SSE]
    Runner --> Agent[Claude Agent Loop]
    Runner --> Judge[LLM-as-Judge]
    Runner --> Scanner[Security Scanner]
    MCPClient -.spawns.-> Target[Target MCP Server]
    Agent --> Anthropic[Anthropic API]
    Judge --> Anthropic

Project structure

mcp-test-bench/
├── apps/
│   ├── cli/          # mcpbench binary (tsup build → dist/index.js)
│   └── web/          # Next.js 15 App Router dashboard
├── packages/
│   └── core/         # MCP client, eval runner, judge, scanner, DB schema
├── examples/
│   ├── ci/           # GitHub Actions workflow + example config
│   └── demo/         # Ready-to-run YAML configs for public MCP servers
├── docs/             # Architecture doc, roadmap, demo gif
└── scripts/          # DB seed and demo helper scripts

Roadmap

Custom judge models — swap the judge to any OpenAI-compatible endpoint via a judge.model config key.
Plugin scanners — a ScannerPlugin interface so community security checks can ship as npm packages.
Hosted mode — optional Turso/libsql backend so teams can share results across machines.
Replay mode — re-score already-saved turns without re-running the agent when rubrics change.
Scenario library — shareable community YAML packs, one per common server type, importable via mcpbench import.

License

MIT — see LICENSE.

Built autonomously by autodev, a multi-agent orchestrator I designed. Each commit in this repo was authored by me; the implementation work was performed by Sonnet under the orchestrator’s control. Read the orchestrator’s README to see how.

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