Give AI agents safe, gated access to your codebase

Coding Tools MCP

English | 简体中文

Give any AI chat or agent a safe pair of hands on your codebase.

Coding Tools MCP is a model-neutral coding runtime served over the
Model Context Protocol: file reading and
search, structured multi-file patches, command execution, interactive
sessions, and git - one server that any MCP client can drive. Claude Desktop,
Claude Code, Codex, Cursor, Cline, VS Code, Windsurf, Gemini CLI, or an agent
you build yourself gets the default catalog of 18 battle-tested tools, confined
to one workspace and gated by permission modes.

Why people use it

• It turns a chat app into a coding agent. Claude Desktop - or any MCP
  chat client - gets real repo access with the subscription you already have.
  No extra product required.
• Safety is the product, not an afterthought. One workspace root per
  server. Absolute paths, .. traversal, and symlink escapes are rejected.
  Permission modes gate network access, shell expansion, inline scripts, and
  destructive commands. On Linux, Landlock adds
  kernel-level filesystem confinement.
• It is model- and vendor-neutral. A truthfully annotated, mode-aware
  catalog - no profile switching, no annotation games. Swap models or clients
  freely; the runtime contract stays put.
• It is engineered for context windows. Results are summarized, paginated,
  and capped by design; serialized tool-result bytes dropped 37%
  release-over-release on the deterministic dogfood workload with unchanged
  task completion.

Quickstart

Run it with whichever toolchain you already have (the server is Python ≥ 3.11
from PyPI; the npm package is a thin launcher that starts it via uv or
pipx):

uvx coding-tools-mcp --stdio --workspace /path/to/repo   # Python toolchain
npx coding-tools-mcp --stdio --workspace /path/to/repo   # Node toolchain

Wire it into Claude Desktop, Claude Code, Codex, Cursor, VS Code, Windsurf,
Gemini CLI, or Cline - the JSON is the same everywhere (swap uvx for npx
if you prefer Node):

{
  "mcpServers": {
    "coding-tools": {
      "command": "uvx",
      "args": ["coding-tools-mcp", "--stdio", "--workspace", "/path/to/repo"]
    }
  }
}

Then ask your client: "run the test suite and fix the first failure."

Prefer HTTP? Drop --stdio and the server speaks Streamable HTTP on
http://127.0.0.1:8765/mcp. Both protocol eras are served on either
transport: MCP 2026-07-28 in full, with tools as the only advertised
capability, and the handshake era 2025-11-25 with 2025-06-18
compatibility. Neither has sessions. A one-line installer, per-client
walkthroughs, and troubleshooting live in
docs/quickstart.md and
docs/mcp-client-config.md.

Seven things to try

1. Make Claude Desktop your coding agent. The config above is all it
takes - the chat window you already pay for can now read, patch, test, and
commit-review a real repository.

2. Code on your own machine from anywhere.

CODING_TOOLS_MCP_AUTH_MODE=bearer ./integrations/tunnels/tunnel.sh cloudflared /path/to/repo

Loopback bind + authenticated HTTPS tunnel (cloudflared, ngrok, or
Microsoft Dev Tunnel). Point claude.ai on your phone at
https://<tunnel-host>/mcp and drive your home workstation from anywhere.
ChatGPT and Grok connect through their connector settings the same way.
Bearer tokens and OAuth 2.1 + PKCE (with RFC 7591 dynamic registration) are
built in. → docs/remote-mcp.md

3. Let an agent loose on untrusted code - inside a disposable sandbox.

docker build -t coding-tools-mcp-sandbox:local .
docker run --rm --init -it -p 8765:8765 -v "$PWD:/workspace" coding-tools-mcp-sandbox:local

A containerized server with toolchains and caches preconfigured, safe to point
at a sketchy PR and destroy afterwards. → docs/docker.md

4. Spin up a cloud sandbox with one MCP call. The bundled
Cloudflare Worker control plane exposes
start_coding_tools_sandbox as an MCP tool: one call dispatches a GitHub
Actions runner that boots the Docker sandbox and publishes it behind an
authenticated Cloudflare Tunnel. Ephemeral compute, no server of your own.

5. Drive it from a GUI.

python -m pip install "coding-tools-mcp[desktop]"
coding-tools-mcp-desktop

Per-workspace profiles, server and tunnel start/stop, credential setup with
clipboard helpers, live health checks. English and 简体中文.

6. Keep an interactive command alive. exec_command starts a REPL or
debugger under a real PTY; write_stdin feeds it across turns; read_output
pages long output; kill_command cleans up. Long-running processes are
first-class, with deadline watchdogs and bounded buffers.

7. Give your own agent production-grade hands. Building an agent loop with
the Anthropic SDK or anything else? Don't hand-roll file and exec tools -
speak MCP to this server and inherit the whole safety boundary. →
docs/embedding.md

The tool catalog

The registry contains 19 truthfully annotated tools. The default safe and
trusted modes advertise 18; dangerous also advertises
request_permissions, the only mode in which that tool can grant anything.
apply_patch and apply_changes are the file-mutation primitives: both are
staged, baseline-checked, atomic across files, and support rollback.

Root AGENTS.md/CLAUDE.md files load automatically and come back in the
instructions of initialize, or of server/discover for a client that
never handshakes. Tool content is concise agent-facing text;
structuredContent carries the complete machine result. Schemas and result
envelopes: docs/tools-and-schemas.md ·
docs/runtime-contract-v0.3.md.

Safety Boundary

Recursive listing and search exclude .git, node_modules, build outputs,
virtualenvs, and caches. Commands run with workspace-bound cwd, scrubbed
environment, timeouts, and output caps. Linux hosts with Landlock get
kernel-enforced filesystem confinement; other platforms get an explicit
warning - this is still not a complete OS sandbox, so use the Docker image or
a VM for genuinely untrusted work. Details:
SECURITY.md · docs/security-boundary.md ·
docs/permission-modes.md

Telemetry

The server sends anonymous usage telemetry (per-tool success/latency counters
and version/platform dimensions - never paths, arguments, commands, or file
contents) to help prioritize fixes. Disable it with
CODING_TOOLS_MCP_TELEMETRY=off or DO_NOT_TRACK=1; it is automatically off
in CI. CODING_TOOLS_MCP_TELEMETRY=debug prints every event to stderr instead
of sending. The full event list and guarantees are in
docs/telemetry.md.

Evidence, Dogfood and SWE-bench

Every release ships through a tag-triggered pipeline in which the compliance
suite, real-workload benchmark, and SWE-bench harness run from the same commit
that publishes to PyPI and npm - both via trusted publishing, npm with
provenance. Dogfood efficiency metrics are reproducible (make dogfood-smoke)
and checked in under reports/. This repository does not claim a
model-generated SWE-bench leaderboard result - see
docs/swe-bench.md for exactly what is and is not
measured. More: COMPLIANCE.md · BENCHMARK.md ·
docs/dogfood.md

Documentation

Development

python -m pip install -e ".[dev]"
make ci        # lint, typecheck, tests, protocol/integration suites, gates

The full gate matrix is in docs/ci-and-tests.md.