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Gossiphs

@williamfzc

Repository

About Gossiphs

"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

Basic information

Category

Other

Transports

stdio

Publisher

williamfzc

Submitted by

williamfzc

Config

Add this server to your MCP-compatible client using the configuration below.

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

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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.

Comments

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