Code Analysis
STDIOAI models understand and analyze codebases through natural language conversations.
AI models understand and analyze codebases through natural language conversations.
A Model Context Protocol (MCP) server that enables AI models to understand and analyze codebases through natural language conversations.
Natural Code Exploration: Ask high-level questions about your codebase
"What are all the different payment providers integrated in the system?"
Deep Code Understanding: Extract insights about data models and system architecture
"How does the user authentication flow work from frontend to database?"
Dynamic Analysis: Trace data flows and understand system relationships
"Show me all the places where we calculate transaction fees"
This tool is a simpler alternative to more sophisticated code analysis tools / copilot like Aider. While it lacks the advanced code analysis capabilities and robustness of tools like Aider, it offers a lightweight solution for codebase exploration if you already have a Claude Pro subscription. The main advantages are:
Note that due to its simpler approach to code analysis, it may make more errors or provide less detailed insights compared to more specialized tools.
git clone https://github.com/saiprashanths/code-analysis-mcp.git cd code-analysis-mcp
Install Claude Desktop App. For more instructions on setting up an MCP Server with Claude Desktop app, see docs
Configure the server in Claude Desktop's configuration file:
~/Library/Application Support/Claude/claude_desktop_config.json
%APPDATA%\Claude\claude_desktop_config.json
{ "mcpServers": { "code-analysis": { "command": "uv", "args": [ "--directory", "/PATH/TO/YOUR/REPO", "run", "code_analysis.py" ] } } }
Restart Claude Desktop and start analyzing your code!
Ensure the tool is correctly setup
code-analysis
tools
analyze_code_repository
prompt
Here's how you might use the tool to understand a complex codebase:
Start by clicking on the analyze_code_repository
prompt shown below. That will guide you through the process
to initialize the repo and get started.
You: How does the transaction processing work in this system? Claude: Let me analyze the data flow for transactions. Looking at the codebase, I can see that transaction processing happens through several key components: 1. Raw transaction data is captured in the `TransactionEvent` model with fields for: - Payment status - Amount - Fee calculations - Processing timestamps 2. This data is then processed through the PaymentSummary model, which: - Validates payment provider responses - Calculates processing fees - Records transaction status Would you like me to explain any part of this in more detail? You: What are the key database tables involved? Claude: The main tables in the transaction processing pipeline are: 1. transaction_events: Stores raw payment events 2. payment_summary: Processed transaction records 3. payment_providers: Provider configuration 4. merchant_accounts: Account-specific settings Let me know if you'd like to see the schema for any of these tables!
Initialize a code repository for analysis:
initialize_repository("/path/to/repo") # Output: Successfully initialized code repository at: /path/to/repo
Retrieve information about the initialized repository:
get_repo_info() # Output: # Code Repository Information: # Path: /path/to/repo # Exists: True # Is Directory: True # Found .gitignore file
Examine repository file structure:
get_repo_structure(depth=2) # Output: # 📁 src/ # 📁 api/ # 📄 routes.py # 📄 models.py # 📁 utils/ # 📄 helpers.py # 📄 main.py
Read and analyze specific files:
read_file("src/api/models.py") # Output: # File: src/api/models.py # Language: python # Size: 2.3 KB # # [File contents...]
This project is licensed under the MIT License - see the LICENSE file for details.