mcpField
G
GenPark AST Diff Semantic PR Summarizer

GenPark AST Diff Semantic PR Summarizer

Local
genpark.aiVersion Control7Updated 1w ago

About GenPark AST Diff Semantic PR Summarizer

A Python MCP server that analyzes git diffs as semantic AST changes, classifies breaking signature changes, and produces structured pull request summaries. It is for development workflows that need change-level summaries beyond raw diff text.

Key features

  1. 01Semantic AST delta trees from git diffs
  2. 02Classification of breaking signature changes
  3. 03Structured pull request summaries
  4. 04Python 3 standard-library implementation

Use cases

  1. 01Summarizing semantic changes in a pull request
  2. 02Identifying breaking signature changes in a diff

MCP support

Runs as an MCP server that can be added to Claude Desktop or Cursor configuration.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbgenpark-ast-diff-semantic-pr-summarizer-skill.mcpb

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

genpark-ast-diff-semantic-pr-summarizer-skill

Verified by Smithery LinkedIn Website

Synthesize semantic AST delta trees across git diffs, classify breaking signature changes, and output structured PR summaries

Quick Installation & MCP Integration

Add to your Claude Desktop or Cursor configuration:

{
  "mcpServers": {
    "genpark-ast-diff-semantic-pr-summarizer-skill": {
      "command": "python",
      "args": ["mcp_server.py"]
    }
  }
}

Zero Dependencies

Implemented entirely in Python 3 standard library.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.