AI导哈

代码搜索与分析服务

TabbyML MCP Server是一个连接TabbyML与AI助手的桥梁，用于搜索和理解任何代码库。

  • by cbusillo
  • Python
  • 本地部署
  • 更新于
在 GitHub 查看 cbusillo/tabby-mcp-server

使用说明

TabbyML MCP服务器

MCP服务器桥将TabbyML连接到AI助手，用于搜索和理解任何代码库。

先决条件

  • TabbyML在本地运行（默认值：http://localhost:8080)
  • TabbyML身份验证令牌
  • Python 3.12+
  • uv包管理器

设置

# Clone and install
git clone https://github.com/cbusillo/tabby-mcp-server.git
cd tabby-mcp-server
uv sync --all-groups

# Set up your TabbyML token (optional - can also pass via MCP config)
echo "TABBY_TOKEN=your_tabby_token_here" > .env

使用Claude CLI快速设置

# Add the MCP server to Claude Code
claude mcp add-json tabby-mcp-server '{
  "command": "uv",
  "args": ["run", "--directory", "/path/to/tabby-mcp-server", "tabby-mcp-server"],
  "env": {
    "TABBY_TOKEN": "your_tabby_token_here",
    "TABBY_URL": "http://localhost:8080"
  }
}'

# If using absolute path to uv (recommended)
claude mcp add-json tabby-mcp-server '{
  "command": "/opt/homebrew/bin/uv",
  "args": ["run", "--directory", "/path/to/tabby-mcp-server", "tabby-mcp-server"],
  "env": {
    "TABBY_TOKEN": "your_tabby_token_here"
  }
}'

环境变量

  • TABBY_TOKEN - 必需：您的TabbyML身份验证令牌
  • TABBY_URL -可选：TabbyML服务器URL（默认值： http://localhost:8080)

工具

  • list_repositories -列出TabbyML中索引的所有存储库
  • search_code -在索引存储库中搜索代码模式
  • chat_with_context -使用存储库上下文与TabbyML AI聊天
  • search_documentation -在文档存储库中搜索信息

发展

uv run test          # Run tests
uv run format        # Format code
uv run lint          # Lint code
uv run update-graphql # Update GraphQL client from TabbyML

更新GraphQL客户端

当TabbyML更新其GraphQL模式或您修改查询时：

# Fetch latest schema and regenerate client code
uv run update-graphql

# This command will:
# 1. Fetch the latest schema from TabbyML
# 2. Convert it to GraphQL SDL format
# 3. Generate typed Python client code
# 4. Update src/tabby_mcp_server/graphql_client/

备注：确保TabbyML正在运行 TABBY_TOKEN 在更新之前设置。

运行集成测试

集成测试需要一个正在运行的TabbyML实例：

# Skip integration tests (default)
uv run test

# Run all tests including integration tests
TABBY_TOKEN=your_token pytest tests/test_integration.py -v

# Run specific integration test
TABBY_TOKEN=your_token pytest tests/test_integration.py::TestTabbyIntegration::test_real_tabby_connection -v

集成测试标记为 @pytest.mark.skipif 并且在以下情况下将被跳过：

  • TabbyML未运行
  • TABBY_TOKEN 未设置
  • 与TabbyML的连接失败

可选：Git预提交钩子

创建 .git/hooks/pre-commit:

#!/bin/bash
set -e
uv run format
uv run lint
uv run test

然后： chmod +x .git/hooks/pre-commit

需求

  • TabbyML在本地运行（默认值：http://localhost:8080)
  • Python 3.12+（兼容3.13）
  • 紫外线

文档代理

tabby-mcp服务器包括一个内置的文档代理，用于解决多语言文档站点的TabbyML超时问题。看 DOC_PROXY.md 了解详情。

# Start proxy for any documentation site
uv run tabby-doc-proxy --url https://docs.example.com/

# Configure TabbyML to use: http://localhost:8888/

您项目的CLAUDE.md示例

将此添加到项目的CLAUDE.md中，以利用TabbyML进行代码理解：

# CLAUDE.md

Project instructions for AI assistance.

## Available Tools

### TabbyML Integration
When working with this codebase, use the TabbyML MCP tools:
- `list_repositories` - View all indexed repositories
- `search_code` - Search for patterns, classes, methods in the codebase
- `chat_with_context` - Ask questions about the code with repository context
- `search_documentation` - Search in documentation repositories

## Code Search Strategy

1. **Before making changes**:
   - Use `search_code` to find similar implementations
   - Use `chat_with_context` to understand complex code sections
   - Use `search_documentation` for API references and guides

2. **Understanding existing code**:
   - List repositories to see what's available
   - Search for usage patterns before implementing new features
   - Ask contextual questions about specific repositories

## Example Workflow

When implementing a new feature:
1. First use `list_repositories` to see available codebases
2. Use `search_code` to find similar implementations
3. Use `chat_with_context` to understand the patterns
4. Check `search_documentation` for best practices
5. Implement following the discovered patterns

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