verimcp
A transparent MCP proxy that independently re-verifies tool-call claims instead of trusting them, paired with devmcp — the git/CI server it's proven against.
README
A trusted agent stack for MCP
<!-- mcp-name: io.github.rudranaresh0201/verimcp -->
Listed on the official MCP Registry as io.github.rudranaresh0201/verimcp and io.github.rudranaresh0201/devmcp.
Problem
MCP tool calls are trusted by default. When a server says a commit succeeded, a file was written, or a CI check passed, the Host has no way to know if that's true — isError: false only means the tool didn't crash, not that it did what it claimed. An agent wired up to git, CI, or infrastructure through MCP is one confidently-wrong tool response away from acting on a lie.
Solution
verimcp is a transparent proxy that sits between an MCP Host and a backend server. It forwards every message unchanged, except: for calls it knows how to check, it independently re-derives the real outcome (re-reads the file, re-checks the git log, re-runs the safe parts of a CI step) and compares that against what the backend claimed. A lie gets rewritten into a real error before the Host ever sees it as a success.
devmcp is the backend it's proven against — a real git/CI MCP server, deliberately built with the full protocol surface (tools, resources, prompts, roots, sampling), because a proxy is only as convincing as what it's shown catching.
Host <--stdio--> verimcp (proxy + verification) <--stdio--> devmcp (git/CI server)
Both are independently installable and have no import dependency on each other — verimcp works in front of any MCP backend (proven against the official mcp-server-git too, not just devmcp), and devmcp works with any Host directly, unproxied.
See it work
pip install verimcp devmcp-server
python scripts/demo.py
That spins up two real subprocess pipes and prints the raw JSON-RPC exchange: one where a backend fabricates a commit hash and verimcp catches it (isError: true, with the real reason), one where a backend writes a real file and verimcp's independent check confirms it and lets the response through unchanged. No mocks — real git repos, real subprocesses, real proxy.
The same claim, proven at larger scale: tests/test_adversarial_corpus.py runs 6 different fabrications (fake commit, fake branch, false CI pass, fake resource read, wrong-shape fake commit, a write that never touched disk) through the real proxy pipe. verimcp catches 6/6.
Technical summary
- Correctness verifiers — one per tool/resource with an independently-checkable postcondition:
write_file(disk hash-compare),git_commit/git_branch(re-derived from real git state),run_ci_pipeline(self-consistency + re-execution of steps marked safe to re-run), plus resource reads (repo://status,repo://log,repo://file/{path}). New verifiers are a plugin system (importlib.metadata.entry_points, the same mechanism pytest/Black use) — seedocs/writing-a-verifier.md. - Policy gates — allow/deny/require-approval rules evaluated before a call reaches the backend, for the calls that have no objective truth to check (sampling rate limits, arbitrary tool-name/argument policy via YAML, human-in-the-loop approval over real MCP
elicitation/create). - Audit + replay — every call verimcp handles is logged and served back as a real
verimcp://auditMCP resource;verimcp replayre-runs recorded traffic against a new policy to backtest "would this have changed anything." - Observability — every call gets an OpenTelemetry span/metric using the GenAI semantic conventions, exportable to any OTLP collector.
- Proven against real clients, not just our own tests — the official MCP Inspector and VS Code's native MCP support (Copilot Chat) each caught a real bug our own test suite never triggered, now fixed with regression tests.
Deeper design reasoning (why prompts/roots get no verifier on principle, why policy is a separate concept from verification, etc.) lives in docs/adr/ for anyone who wants to go that deep — the summary above is everything needed to use or evaluate the project.
Getting started
As a user — install straight from PyPI:
pip install verimcp devmcp-server
verimcp -- devmcp --repo-path ./some-repo
Note on names: the PyPI distribution is
devmcp-server(devmcpwas blocked by PyPI's typosquat-similarity check against an unrelated existing package), but the Python import and CLI command are both still plaindevmcp— nothing above changes if you're reading devmcp's own source.
In Docker — no local Python/git needed:
docker build -t verimcp-devmcp .
docker run -i -v /path/to/your/repo:/repo verimcp-devmcp
As a contributor — editable installs from this repo:
pip install -e ".[dev]"
pip install -e "./devmcp[dev]"
pytest tests devmcp/tests # 136 tests, real subprocess + real git repo, nothing mocked
ruff check src tests devmcp/src devmcp/tests
python scripts/inspector_smoke_test.py # verify against the real MCP Inspector client (needs node/npx)
Try the observability yourself
# spans/metrics print to stderr (verimcp's stdout is the live MCP protocol channel)
verimcp --otel-exporter console -- devmcp --repo-path ./some-repo
# or point a real collector, Jaeger, or Grafana Agent at it
pip install "verimcp[otel]"
verimcp --otel-exporter otlp --otel-endpoint localhost:4317 -- devmcp --repo-path ./some-repo
Omitting --otel-exporter entirely means zero telemetry overhead — the default, same as every other opt-in flag here.
Contributing
See CONTRIBUTING.md — dev setup, how to add a verifier, this project's ADR discipline, and commit/PR conventions. See CHANGELOG.md for release history.
License
MIT — see LICENSE.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。