GitHub
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
Description from the maker (original English)
Model Context Protocol server for Google's Jules AI coding agent
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Pick the AI app you use, then copy the settings.
JULES_API_KEYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"google-jules": {
"command": "npx",
"args": [
"-y",
"google-jules-mcp-server"
],
"env": {
"JULES_API_KEY": "your-value"
}
}
}
}Run this in your terminal
claude mcp add google-jules --env JULES_API_KEY=your-value -- npx -y google-jules-mcp-serverFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"google-jules": {
"command": "npx",
"args": [
"-y",
"google-jules-mcp-server"
],
"env": {
"JULES_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"google-jules": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"google-jules-mcp-server"
],
"env": {
"JULES_API_KEY": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 9, 2026
The registry name was verified with the GitHub account georgeracu, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: JULES_API_KEY
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 2 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: GitHub, Desktop Commander, Cloudflare MCP
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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
MCP server for terminal commands, file operations, and process management
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If you're searching for a way to drive Google's Jules coding agent from Claude, Cursor, VS Code Copilot, or any other MCP client, this is that bridge. google-jules-mcp-server is an unofficial Model Context Protocol server that exposes the full Jules API (v1alpha) as 13 tools, so your assistant can create sessions, poll status, approve plans, and fetch the resulting pull request without you leaving the chat.
Developers running Jules across many repositories at once use it to let their AI assistant manage the whole async workflow: kick off a task, check on it later, and hand back a PR link when it's done, instead of switching to the Jules web app to babysit progress. Every response is validated at runtime through Zod schemas, so a drifted Jules API response fails loudly rather than silently breaking downstream code.
Model Context Protocol (MCP) server for Google's Jules AI coding agent — unofficial. Lets AI assistants like Claude create and manage asynchronous coding tasks through the Jules API v1alpha.
Jules is Google's AI coding agent that executes development tasks in isolated cloud VMs — generating code, fixing bugs, writing tests, updating dependencies, and refactoring across files. This server exposes the full Jules API surface as 13 MCP tools, covering repository sources, session lifecycle, and activity logs.
Tasks run asynchronously and typically complete in 5–60 minutes depending on complexity.
The server is organized by Jules resource domain rather than as one flat file:
src/
├── index.ts # entrypoint: connects the stdio transport ├── server.ts # createServer(): wires the MCP server + all tool registrations ├── core/ # transport-agnostic: auth, HTTP client, retry/backoff, typed errors, logging └── resources/ ├── sources/ # jules_list_sources, jules_get_source ├── sessions/ # session lifecycle (create/list/status/message/approve/output/delete/archive/unarchive) └── activities/ # jules_list_activities, jules_get_activity
Each resource module owns its own Zod schemas, a typed API client, and its MCP tool registrations. Zod schemas are the single source of truth: TypeScript types are inferred from them (z.infer), and every Jules API response is validated at runtime through core/http-client.ts — so a drifted API response fails loudly as a JulesResponseValidationError instead of silently producing undefineds downstream.
core/http-client.ts also centralizes retry-with-backoff (bounded, honors Retry-After) and a typed error hierarchy (JulesAuthError, JulesNotFoundError, JulesRateLimitError, JulesServerError, JulesClientError, JulesNetworkError, JulesResponseValidationError) so callers can distinguish failure modes programmatically rather than pattern-matching error strings.
The package is published on npm as google-jules-mcp-server. Most MCP clients can run it directly via npx — no separate install step needed, skip to step 2.
If you'd rather install it once instead of letting your client invoke npx on every launch:
npm install -g google-jules-mcp-server
This puts a google-jules-mcp binary on your PATH.
Building from source (for contributors, or to run unreleased changes):
git clone https://github.com/georgeracu/google-jules-mcp-server.git
cd google-jules-mcp-server npm install npm run build
Get a key from https://jules.google.com/settings#api. Set it directly in your MCP client's server config (see below) — that's the only place it needs to live for normal use.
If you're building from source and want to run npm run test:smoke or use .env for local scripts:
cp .env.example .env
Claude Desktop — edit the config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": { "jules": { "command": "npx", "args": ["-y", "google-jules-mcp-server"], "env": { "JULES_API_KEY": "your_actual_jules_api_key_here" } } } }
Claude Code:
claude mcp add jules -s user -e JULES_API_KEY=your_actual_jules_api_key_here -- npx -y google-jules-mcp-server
GitHub Copilot CLI:
copilot mcp add jules -e JULES_API_KEY=your_actual_jules_api_key_here -- npx -y google-jules-mcp-server
Or edit ~/.copilot/mcp-config.json directly:
{
"mcpServers": { "jules": { "type": "local", "command": "npx", "args": ["-y", "google-jules-mcp-server"], "env": { "JULES_API_KEY": "your_actual_jules_api_key_here" }, "tools": ["*"] } } }
VS Code (Copilot Chat agent mode) — via terminal:
code --add-mcp '{"name":"jules","command":"npx","args":["-y","google-jules-mcp-server"],"env":{"JULES_API_KEY":"your_actual_jules_api_key_here"}}'
Or add to .vscode/mcp.json (workspace) or via MCP: Open User Configuration (user-level):
{
"servers": { "jules": { "type": "stdio", "command": "npx", "args": ["-y", "google-jules-mcp-server"], "env": { "JULES_API_KEY": "your_actual_jules_api_key_here" } } } }
Cursor — add to ~/.cursor/mcp.json (global) or .cursor/mcp.json (project-only):
{
"mcpServers": { "jules": { "command": "npx", "args": ["-y", "google-jules-mcp-server"], "env": { "JULES_API_KEY": "your_actual_jules_api_key_here" } } } }
OpenAI Codex CLI:
codex mcp add jules --env JULES_API_KEY=your_actual_jules_api_key_here -- npx -y google-jules-mcp-server
Or edit ~/.codex/config.toml directly:
[mcp_servers.jules]
command = "npx" args = ["-y", "google-jules-mcp-server"] env = { JULES_API_KEY = "your_actual_jules_api_key_here" }
If you installed globally instead, replace "command": "npx", "args": ["-y", "google-jules-mcp-server"] with "command": "google-jules-mcp", "args": [] (and any npx -y google-jules-mcp-server in a CLI command above with just google-jules-mcp).
If you're running from a local clone instead, use "command": "node", "args": ["/absolute/path/to/google-jules-mcp-server/build/index.js"] (an absolute path to build/index.js).
Behind a corporate proxy — to support enterprise/corporate proxy setups without requiring any server-side config, the server automatically picks up proxy settings from the environment. The underlying EnvHttpProxyAgent (from undici) honours the standard HTTPS_PROXY, https_proxy, HTTP_PROXY, http_proxy, and NO_PROXY variables. What catches people out is that your MCP client spawns the server as a child process, so a variable exported in ~/.zshrc never reaches a GUI-launched app. Set it in the same env block as your API key:
{
"mcpServers": { "jules": { "command": "npx", "args": ["-y", "google-jules-mcp-server"], "env": { "JULES_API_KEY": "your_actual_jules_api_key_here", "HTTPS_PROXY": "http://proxy.example.com:8080", "NO_PROXY": "localhost,127.0.0.1" } } } }
Proxied requests are broken in 0.2.3 and earlier, where every call fails on a gzipped response the client never decoded. If you hit that, upgrade rather than reconfigure.
Restart your client after editing its config.
Ask your assistant: "List my Jules repositories." You should see the jules server connected with 16 tools available.
| Tool | Purpose |
|---|---|
jules_list_sources | List GitHub repositories connected to Jules |
jules_get_source | Get details (branches, visibility) for one connected repository |
jules_create_session | Start a new asynchronous coding task |
jules_list_sessions | List sessions and their states |
jules_list_stuck_sessions | List sessions awaiting plan approval or user feedback, following pagination automatically |
jules_get_status | Check a session's status and recent activity |
jules_send_message | Send a follow-up instruction to a running session |
jules_approve_plan | Approve a session's execution plan (when requirePlanApproval was set) |
jules_get_session_output | Retrieve the final output (PR details) of a completed session |
jules_delete_session | Permanently delete a session |
jules_archive_session | Archive a session without deleting it |
jules_unarchive_session | Restore an archived session |
jules_wait_for_session | Wait/poll for a session to reach a terminal state |
jules_execute_and_wait | Create a session and wait for it to complete in one call |
jules_list_activities | Get a session's detailed activity log |
jules_get_activity | Get a single activity by ID |
Everything these tools return lands in your assistant's context window, and an autonomous coding session can produce very long agent messages, progress descriptions and plans of hundreds of steps. The server therefore caps what it hands back, and says so in-band whenever it cuts something, so the assistant can decide whether to go and fetch the rest.
| Tool | Cap |
|---|
| jules_get_status | 100 characters per activity i
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