code-analysis-mcp
Documentation
Code Analysis MCP Server
A Model Context Protocol (MCP) server that enables AI models to understand and analyze codebases through natural language conversations.
✨ Highlights
- 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"Limitations
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:
- Cost-Effective: Using your existing Claude Pro subscription means no additional API costs, unlike tools that can get expensive when analyzing large codebases
- Simple Setup: Quick to get started with minimal configuration
- Basic Analysis: Good for high-level code understanding and exploration
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.
🚀 Quick Start
1. Install the server:
git clone https://github.com/saiprashanths/code-analysis-mcp.git
cd code-analysis-mcp2. Install Claude Desktop App. For more instructions on setting up an MCP Server with Claude Desktop app, see docs
3. Configure the server in Claude Desktop's configuration file:
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
{
"mcpServers": {
"code-analysis": {
"command": "uv",
"args": [
"--directory",
"/PATH/TO/YOUR/REPO",
"run",
"code_analysis.py"
]
}
}
}4. Restart Claude Desktop and start analyzing your code!
5. Ensure the tool is correctly setup
- Clicking the tools icon should display the `code-analysis` tools
- Clicking the integrations button should display the `analyze_code_repository` prompt
💡 Example Usage
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!🔧 Available Tools
initialize_repository
Initialize a code repository for analysis:
initialize_repository("/path/to/repo")
# Output: Successfully initialized code repository at: /path/to/repoget_repo_info
Retrieve information about the initialized repository:
get_repo_info()
# Output:
# Code Repository Information:
# Path: /path/to/repo
# Exists: True
# Is Directory: True
# Found .gitignore fileget_repo_structure
Examine repository file structure:
get_repo_structure(depth=2)
# Output:
# 📁 src/
# 📁 api/
# 📄 routes.py
# 📄 models.py
# 📁 utils/
# 📄 helpers.py
# 📄 main.pyread_file
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...]⚙️ Technical Details
- Default scanning depth: 3 levels
- Maximum file size: 1MB
- Maximum lines per file: 1000
- Ignores paths listed in .gitignore
- Local file system access only
- Validates file paths to prevent directory traversal
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
Frequently asked questions
What is code-analysis-mcp?
code-analysis-mcp is a Model Context Protocol (MCP) server listed in the TrackMCP directory.
How do I install code-analysis-mcp?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is code-analysis-mcp open source?
Yes — it is hosted on GitHub at https://github.com/saiprashanths/code-analysis-mcp and has 21 stars.
Related MCP tools
5. Update .env file in the root directory with your mem0 API key: for the Model Context Protocol. Enhance AI assistants with powerful integrations.
Model Context Protocol integration for Python. Enhance AI assistants with powerful Python-based capabilities and integration support.
This repository contains Model Context Protocol (MCP) servers that enable MCP clients (like Claude Desktop or the cline.
🙌 OpenHands: Code Less, Make More for the Model Context Protocol. Enhance AI assistants with powerful integrations. Python-based implementation.
Universal memory layer for AI Agents; Announcing OpenMemory MCP - local and secure memory management. Python-based implementation.
基于大模型搭建的聊天机器人,同时支持 微信公众号、企业微信应用、飞书、钉钉 等接入,可选择ChatGPT/Claude/DeepSeek/文心一言/讯飞星火/通义千问/ Gemini/GLM-4/Kimi/LinkAI,能处理文本、语音和图片,访问操作系统和互联网,支持基于自有知识库进行定制企业智能客服。
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP