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MCP Log Analyzer

README.mdโ€ข3.44 kB
# ๐Ÿ” MCP Log Analyzer The **MCP Log Analyzer** is an AI-powered Streamlit app designed to analyze system log files, identify errors and warnings, and recommend fixes. It uses [FastMCP](https://github.com/langchain-ai/langchain/tree/main/libs/langchain-mcp-adapters), [LangGraph ReAct agents](https://github.com/langchain-ai/langgraph), and [Anthropic Claude](https://www.anthropic.com/) LLM to build a powerful multi-agent system. --- ``` ## ๐Ÿ“ Project Structure โ”œโ”€โ”€ analyzer.py # MCP server with two tools: analyze_logs & suggest_fix โ”œโ”€โ”€ streamlit_ui.py # Streamlit web interface โ”œโ”€โ”€ streamlit_client.py # MCP client invoking tools via LangGraph + Claude โ”œโ”€โ”€ mcp_config_2.json # JSON config for MCP server commands โ”œโ”€โ”€ test_model.py # Placeholder test script โ”œโ”€โ”€ README.md # โ† You're here โ”œโ”€โ”€ temp/ # Temporary files โ”œโ”€โ”€ Test logs/ # Sample or uploaded logs โ”œโ”€โ”€ Screenshots/ # UI screenshots โ”œโ”€โ”€ .venv/ # Python virtual environment ``` ## Create Virtual Environment python -m venv .venv .\.venv\Scripts\activate ## Install Dependencies pip install -r requirements.txt ### If requirements.txt doesn't exist, here are the needed packages: pip install streamlit langchain langgraph langchain-anthropic anyio nest_asyncio pip install mcp langchain-mcp-adapters ## ๐Ÿงช Run MCP Tool Server python analyzer.py ## ๐Ÿง  Run the Streamlit Client App streamlit run streamlit_ui.py ## ๐Ÿงพ Sample mcp_config_2.json { "mcpServers": { "LogAnalyzer": { "command": "{Your-directory}\\uv.EXE", "args": [ "run", "--with", "mcp[cli]", "mcp", "run", "{Your-directory}\\analyzer.py" ] } } } ## ๐Ÿ“ฆ Log File Format Uploaded logs should be a list of JSON objects like: [ { "timestamp": "2025-07-26T12:30:01Z", "level": "ERROR", "component": "DataProcessor", "message": "NullPointerException in AuthService", "stack_trace": "java.lang.NullPointerException..." }, ... ] ## ๐Ÿ“Œ Notes Claude API key is required in streamlit_client.py. Replace 'Your-API-Key' with your actual key. If using TCP transport instead of stdio (recommended on Windows), modify the server and client configs accordingly. You can customize or add new tools in analyzer.py and expose them via @mcp.tool(). ## ๐Ÿง  Credits Built using: LangChain MCP :- https://github.com/langchain-ai/langchain/tree/main/libs/langchain-mcp-adapters Anthropic Claude :- https://www.anthropic.com/ Streamlit :- https://streamlit.io/ LangGraph Agents :- https://github.com/langchain-ai/langgraph ## ๐Ÿ› ๏ธ Future Improvements Add support for batch analysis or CSV uploads Save session history Enable tool reordering / multiple MCPs Deploy to Hugging Face / Streamlit Cloud ## Screenshots :- <img width="641" height="801" alt="Streamlit_app_1" src="https://github.com/user-attachments/assets/f87c6501-8067-406d-8134-709cbd0f1cee" /> <img width="691" height="829" alt="Streamlit_app_2" src="https://github.com/user-attachments/assets/0be2c09e-f943-4a46-abb0-b80a616f1821" /> <img width="598" height="822" alt="Streamlit_app_3" src="https://github.com/user-attachments/assets/2e1d7248-8087-47ec-bb4d-c2dbc5174d62" /> <img width="652" height="267" alt="python_terminal" src="https://github.com/user-attachments/assets/6a83c6d2-56b5-48fc-afa7-2c9eebb7b5ad" /> ## ๐Ÿ“ƒ License MIT License

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