Code2prompt
Transforms complex codebases into structured summaries optimized for language models through an MCP server, facilitating better code understanding and analysis workflows. Directly relevant as an MCP server.
About this tool
Code2prompt
Category: Repository Code Analysis MCP Servers
Tags: mcp, code-analysis, summarization, language-models
Overview
Code2prompt is an MCP server designed to generate contextual and structured summaries from complex codebases. It facilitates the integration of code repositories with language models, optimizing them for better understanding and analysis workflows by AI assistants.
Features
- MCP Server: Runs as an MCP server to handle code analysis requests.
- Contextual Prompt Generation: Produces contextual prompts from codebases, aiding AI and language models in comprehending code structure and content.
- Structured Summaries: Generates structured summaries to bridge code and AI language models, enhancing analysis workflows.
- Rust-Powered Analysis: Utilizes the high-performance
code2prompt-rsRust library for code analysis. - Optimized for AI Consumption: Outputs are formatted to be directly usable by language models, improving the efficiency of code understanding tasks.
- Integration Ready: Can be used as part of automated workflows for code analysis and AI-assisted development.
Installation
- Uses Rye for dependency management.
- Dependencies specified in
pyproject.toml. - Installation instructions available in the repository documentation.
License
MIT License
Source
https://github.com/odancona/code2prompt-mcp
Pricing
No pricing information is provided; the project is open source under the MIT License.
Surveys
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Information
Websitegithub.com
PublishedMay 14, 2025