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Sourcerer MCP Server

@st3v3nmw

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

st3v3nmw

提交者

Stephen

关于 Sourcerer MCP Server

An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need.

#sourcerer#code-search#ai-agents

Sourcerer MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "sourcerer": {
      "command": "sourcerer",
      "env": {
        "OPENAI_API_KEY": "your-openai-api-key",
        "SOURCERER_WORKSPACE_ROOT": "/path/to/your/project"
      }
    }
  }
}

Sourcerer MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Sourcerer MCP?

Sourcerer MCP is an MCP server for semantic code search and navigation. It helps AI agents find specific functions, classes, and code chunks using conceptual search instead of reading entire files, reducing token usage and cognitive load.

How to use Sourcerer MCP?

Install via go install or Homebrew. Configure with an OpenAI API key and workspace root path, then add the server to your MCP client (e.g., Claude Code or mcp.json). Once configured, agents can call tools like semantic_search and get_source_code to query and retrieve code.

Key features of Sourcerer MCP

  • Semantic search by concept and functionality
  • Retrieve specific code chunks by stable ID
  • Uses Tree-sitter for AST-based code parsing
  • Automatically re-indexes changed files via file watching
  • Respects .gitignore rules
  • Stores embeddings persistently in .sourcerer/db/

Use cases of Sourcerer MCP

  • AI agents finding relevant code without scanning entire files
  • Navigating directly to functions, classes, or methods by name or purpose
  • Reducing token consumption during code comprehension tasks
  • Conceptually searching a codebase for patterns and dependencies

常见问题

1

What are the requirements to run Sourcerer MCP?

An OpenAI API Key is required for generating embeddings. The project must be a Git repository, and you should add `.sourcerer/` to your `.gitignore` to ignore the vector database directory.

2

What MCP tools does Sourcerer MCP provide?

It provides `semantic_search` (find code by concept), `get_source_code` (retrieve a chunk by ID), `index_workspace` (manually trigger re-indexing), and `get_index_status` (check indexing progress).

3

What programming languages are currently supported?

Only Go is currently supported. Python, TypeScript, and JavaScript are planned. Adding support for new languages requires writing Tree-sitter queries.

4

How does Sourcerer MCP handle file changes?

It watches for file changes using `fsnotify`, respects `.gitignore` via `git check-ignore`, and automatically re-indexes changed files while tracking modification times.

5

Where is the vector database stored?

The vector database is stored persistently in the `.sourcerer/db/` directory within the workspace root.

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