Gossiphs

Name: Gossiphs
Author: williamfzc

@williamfzc

Gossiphs について

"Zero setup" & "Blazingly fast" general code file relationship analysis. With Python & Rust. Based on tree-sitter and git analysis. Support MCP and ready for AI🤖

#python #code-navigation #codebase

基本情報

カテゴリ

その他

トランスポート

stdio

公開者

williamfzc

投稿者

williamfzc

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "gossiphs": {
      "url": "http://127.0.0.1:8000/sse"
    },
    "gossiph-cmd": {
      "command": "gossiphs-mcp",
      "args": [
        "server"
      ]
    }
  }
}

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Gossiphs?

Gossiphs is a "zero setup" and "blazingly fast" code file relationship analysis tool that uses tree-sitter and git history to build a graph of symbol definitions and references across files. It is designed for developers and AI integration, providing a Python SDK and support for the Model Context Protocol (MCP).

How to use Gossiphs?

Install via pip install gossiphs, then use the Python API to configure a project path and create a NetworkX graph of file relationships. Alternatively, use the CLI to export CSV files or start an HTTP service. For MCP integration, see the dedicated MCP README.

Key features of Gossiphs

Zero repository-specific configuration required.
Blazingly fast analysis using tree-sitter and git analysis.
Supports multiple languages (Rust, Python, TypeScript, JavaScript, Golang, Java, Kotlin, Swift).
Can export relationship graphs as CSV or serve via HTTP.
MCP support for seamless AI integration.
Acceptable accuracy (88–94% coverage of LSP edges in tested repos).

Use cases of Gossiphs

Guiding context during IDE development.
Facilitating code browsing on websites.
Impact analysis of code changes in CI systems.
Querying references and definitions across a codebase for complex analysis.

FAQ from Gossiphs

What is the main advantage over stack-graphs or LSP?

Gossiphs trades high precision for zero configuration and broad language support, using commit histories to refine edges instead of complex scope analysis. It offers acceptable accuracy with lower setup cost.

Which languages are currently supported?

Supported languages: Rust, Python, TypeScript, JavaScript, Golang, Java, Kotlin, and Swift. Rules for additional languages can be added via tree-sitter queries.

How does it achieve zero setup?

Gossiphs does not require per-repository configuration; it automatically extracts imports/exports and uses commit history to infer relationships between files.

Where does the data live?

Analysis runs locally – the graph is built in memory and can be exported to CSV or used via HTTP service. No remote storage is mentioned.

What are the known limitations?

Static analysis cannot handle dynamic binding, so it may miss some references that LSP would find. Precision is lower than LSP but sufficient for many use cases.

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