Code2Flow MCP Server
by MCP-Mirror
This project wraps the code2flow command-line tool as an MCP (Model Context Protocol) server. It allows AI applications to generate and access code call graphs through a standardized MCP protocol.
Last updated: N/A
What is Code2Flow MCP Server?
The Code2Flow MCP Server is a wrapper around the code2flow command-line tool, exposing its functionality through the Model Context Protocol (MCP). It enables AI applications to generate and access code call graphs in a standardized way.
How to use Code2Flow MCP Server?
To use the server, first clone the repository, install the dependencies (including code2flow), and then run the server.py script. You can interact with the server using an MCP client, sending requests to generate call graphs, check code2flow version, or analyze code complexity. Configuration options are available for specifying source paths, output paths, languages, and exclusion/inclusion patterns.
Key features of Code2Flow MCP Server
Analyzes source code and generates call graphs
Supports multiple programming languages (Python, JavaScript, Ruby, PHP)
Provides service through the MCP protocol for easy integration with AI applications
Outputs images in PNG format
Offers version checking and code complexity analysis
Use cases of Code2Flow MCP Server
Integrating code call graph generation into AI-powered code analysis tools
Providing AI models with context about code structure and dependencies
Automating code documentation and visualization
Enhancing code understanding and debugging workflows
FAQ from Code2Flow MCP Server
What is code2flow?
What is code2flow?
code2flow is a command-line tool that generates call graphs from source code.
What is MCP?
What is MCP?
MCP stands for Model Context Protocol, a standardized protocol for communication between AI models and tools.
What languages are supported?
What languages are supported?
The server supports Python, JavaScript, Ruby, and PHP.
How do I install the server?
How do I install the server?
Clone the repository, create a virtual environment, install the dependencies using pip install -r requirements.txt, and install code2flow using pip install code2flow.
How do I generate a call graph?
How do I generate a call graph?
Use an MCP client to call the generate_call_graph tool, providing the source paths and language as parameters.