Sourcerer

by st3v3nmw

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AI

About

MCP for semantic code search & navigation that reduces token waste

Explore

- 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/

Setting up with Highlight

This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:

Download and install Highlight from highlightai.com/download
Navigate to the plugins tab and select "Add Custom Plugin"
Configure the plugin with the settings below

Plugin Name Sourcerer

Command (node, npx, python, etc.)

Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
Enable "Start Automatically" if you want the plugin to start when Highlight launches

Setting up with Cursor

Open Cursor's settings
Navigate to the MCP section
Click "Add new Global MCP server"
Copy and paste the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Claude Desktop

Open Claude Desktop's settings
Go to the Integrations tab
Click "Add MCP Server"
Use the configuration below based on your operating system

Note: Make sure Claude Desktop is running before attempting to connect.

JSON

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

JSON

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

JSON

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

Setting up with Windsurf

Open Windsurf's preferences
Go to the Integrations section
Click "Add MCP Integration"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

Setting up with Cline

Open Cline's settings
Navigate to the Integrations section
Click "Add MCP Server"
Copy and paste the configuration below based on your operating system

Note: Make sure to restart Cline after adding the configuration.

JSON

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

JSON

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

JSON

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

Setting up with Goose

Open Goose's preferences
Go to the Integrations tab
Select "MCP Servers"
Click "Add New Server"
Use the configuration below based on your operating system

JSON

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

JSON

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

JSON

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

From the repository

- OpenAI API Key: Required for generating embeddings (local embedding support planned)
- Git: Must be a git repository (respects .gitignore files)
- Add .sourcerer/ to .gitignore: This directory stores the embedded vector database

`semantic_search`

Find code by concept/functionality

`get_source_code`

Retrieve specific chunks by ID

`index_workspace`

Manually trigger re-indexing

`get_index_status`

Check indexing progress

- semantic_search: Find code by concept/functionality
- get_source_code: Retrieve specific chunks by ID
- index_workspace: Manually trigger re-indexing
- get_index_status: Check indexing progress

This approach allows AI agents to find relevant code without reading entire files,
dramatically reducing token usage and cognitive load.

Claude Desktop / Cursor

Paste into your MCP client config file to install this server.

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

McpServers

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

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.

Demo

Requirements

- OpenAI API Key: Required for generating embeddings (local embedding support planned) - Git: Must be a git repository (respects .gitignore files) - Add .sourcerer/ to .gitignore: This directory stores the embedded vector database

Installation

Go

shell
go install github.com/st3v3nmw/sourcerer-mcp/cmd/sourcerer@latest



Homebrew

shell
brew tap st3v3nmw/tap
brew install st3v3nmw/tap/sourcerer



Configuration


Claude Code

shell
claude mcp add sourcerer -e OPENAI_API_KEY=your-openai-api-key -e SOURCERER_WORKSPACE_ROOT=$(pwd) -- sourcerer



mcp.json

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



How it Works


Sourcerer builds a semantic search index of your codebase:

1. Code Parsing & Chunking


- Uses Tree-sitter to parse source files into ASTs
- Extracts meaningful chunks (functions, classes, methods, types) with stable IDs
- Each chunk includes source code, location info, and contextual summaries
- Chunk IDs follow the pattern:

file.ext::TypeName::methodName



2. File System Integration


- Watches for file changes using

fsnotify


- Respects

.gitignore files via git check-ignore


- Automatically re-indexes changed files
- Stores metadata to track modification times

3. Vector Database


- Uses chromem-go for persistent vector storage in

.sourcerer/db/


- Generates embeddings via OpenAI's API for semantic similarity
- Enables conceptual search rather than just text matching
- Maintains chunks, their embeddings, and metadata

4. MCP Tools


-

semantic_search

: Find code by concept/functionality
-

get_source_code

: Retrieve specific chunks by ID
-

index_workspace

: Manually trigger re-indexing
-

get_index_status`: Check indexing progress This approach allows AI agents to find relevant code without reading entire files, dramatically reducing token usage and cognitive load.

Supported Languages

Language support requires writing Tree-sitter queries to identify functions, classes, interfaces, and other code structures for each language. Supported: Go Planned: Python, TypeScript, JavaScript

Contributing

All contributions welcome!

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About

Explore

Setting up with Highlight

From the repository

semantic_search

get_source_code

index_workspace

get_index_status

Claude Desktop / Cursor

McpServers

Demo

Requirements

Installation

Go

Homebrew

Configuration

Claude Code

mcp.json

How it Works

1. Code Parsing & Chunking

2. File System Integration

3. Vector Database

4. MCP Tools

Supported Languages

Contributing

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`semantic_search`

`get_source_code`

`index_workspace`

`get_index_status`