Jules MCP Server
Enables LLM applications to interact with Google's Jules AI coding assistant to manage repositories, coding sessions, and pull requests. It allows users to programmatically create tasks, approve plans, and communicate with the assistant during active coding sessions.
README
Jules MCP Server
A Model Context Protocol (MCP) server for the Google Jules API. This server enables LLM applications to interact with Jules - Google's AI coding assistant - to create sessions, send messages, and manage coding tasks programmatically.
Features
- List Sources: Browse available GitHub repositories connected to Jules
- Create Sessions: Start new coding tasks with Jules
- Send Messages: Communicate with Jules during active sessions
- Approve Plans: Approve Jules's proposed plans before execution
- List Activities: View the work history and conversation for a session
- Create Pull Requests: Convenience tool to create sessions that result in PRs
Prerequisites
- Python 3.10+
- A Jules API key (generate one from the Jules Settings page)
- GitHub repositories installed in Jules
Installation
Using uv (Recommended)
# Clone the repository
git clone https://github.com/yourusername/jules-mcp.git
cd jules-mcp
# Install dependencies with uv
uv sync
Using pip
# Clone the repository
git clone https://github.com/yourusername/jules-mcp.git
cd jules-mcp
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install fastmcp httpx pydantic
Configuration
Set the JULES_API_KEY environment variable with your API key:
export JULES_API_KEY="your-api-key-here"
Usage
Running Standalone
# With uv
uv run python main.py
# With pip/venv
python main.py
MCP Configuration
Add this server to your MCP client configuration. Below are examples for different setups.
Claude Desktop / Claude Code
Add to your mcp.json or claude_desktop_config.json:
{
"mcpServers": {
"jules": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/jules-mcp",
"run",
"python",
"main.py"
],
"env": {
"JULES_API_KEY": "your-jules-api-key"
}
}
}
}
Alternative (without uv)
{
"mcpServers": {
"jules": {
"command": "/absolute/path/to/jules-mcp/.venv/bin/python",
"args": [
"/absolute/path/to/jules-mcp/main.py"
],
"env": {
"JULES_API_KEY": "your-jules-api-key"
}
}
}
}
Available Tools
list_sources
List available GitHub repositories that Jules can work with.
Parameters:
page_size(int, optional): Number of sources to return (1-100, default 30)page_token(str, optional): Pagination token
get_source
Get details about a specific source repository.
Parameters:
source_name(str, required): Resource name (e.g., "sources/github/owner/repo")
list_sessions
List Jules sessions (coding tasks).
Parameters:
page_size(int, optional): Number of sessions to return (1-100, default 30)page_token(str, optional): Pagination tokenactive_only(bool, optional): Filter to only show active sessions
get_session
Get details about a specific session.
Parameters:
session_name(str, required): Resource name (e.g., "sessions/abc123")
create_session
Create a new Jules session to work on a coding task.
Parameters:
prompt(str, required): The coding task descriptionsource(str, required): Resource name of the repositorybranch(str, optional): Branch to usetitle(str, optional): Session titlerequire_plan_approval(bool, optional): Wait for plan approval before executing
send_message
Send a follow-up message to an active session.
Parameters:
session_name(str, required): Resource name of the sessionmessage(str, required): Message to send
approve_plan
Approve Jules's plan for a session in AWAITING_PLAN_APPROVAL state.
Parameters:
session_name(str, required): Resource name of the session
list_activities
List activities (work history) for a session.
Parameters:
session_name(str, required): Resource name of the sessionpage_size(int, optional): Number of activities to return (1-100, default 50)page_token(str, optional): Pagination token
create_pull_request
Create a session that will result in a pull request.
Parameters:
prompt(str, required): Description of changes to makesource(str, required): Resource name of the repositorybranch(str, optional): Base branch for the PRtitle(str, optional): Title for the session/PR
Session States
Sessions progress through these states:
QUEUED- Session is waiting to startPLANNING- Jules is creating a planAWAITING_PLAN_APPROVAL- Waiting for user to approve the planAWAITING_USER_FEEDBACK- Waiting for user inputIN_PROGRESS- Jules is working on the taskPAUSED- Session is pausedFAILED- Session failedCOMPLETED- Session completed successfully
Example Workflow
-
List available repositories:
Use the list_sources tool to see which repos are available -
Create a coding task:
Use create_session with: - prompt: "Add a dark mode toggle to the settings page" - source: "sources/github/myorg/myrepo" -
Monitor progress:
Use get_session to check the session state Use list_activities to see what Jules is doing -
Interact with Jules:
Use send_message if Jules needs clarification Use approve_plan if plan approval is required -
Get the result:
Once session is COMPLETED, check the outputs field for PR URL
API Reference
This server wraps the Google Jules API (v1alpha).
License
MIT
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。