<p align="center"> <img src="https://img.shields.io/badge/Python-3.12-blue?logo=python" alt="Python Badge"/> <img src="https://img.shields.io/badge/MCP-Model_Context_Protocol-purple" alt="MCP Badge"/> <img src="https://img.shields.io/badge/Ollama-LLM-green" alt="Ollama Badge"/> <img src="https://img.shields.io/badge/ChromaDB-VectorDB-orange" alt="ChromaDB Badge"/> <img src="https://img.shields.io/badge/FastAPI-Web_UI-teal" alt="FastAPI Badge"/> <img src="https://img.shields.io/badge/Uvicorn-ASGI_Server-black" alt="Uvicorn Badge"/> </p>

Autonomous Analyst

🧠 Overview

Autonomous Analyst is a local, agentic AI pipeline that:

• Analyzes tabular data
• Detects anomalies with Mahalanobis distance
• Uses a local LLM (llama3.2:1b via Ollama) to generate interpretive summaries
• Logs results to ChromaDB for semantic recall
• Is fully orchestrated via the Model Context Protocol (MCP)

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⚙️ Features

Component	Description
FastAPI Web UI	Friendly dashboard for synthetic or uploaded datasets
MCP Tool Orchestration	Each process step is exposed as a callable MCP tool
Anomaly Detection	Mahalanobis Distance-based outlier detection
Visual Output	Saved scatter plot of inliers vs. outliers
Local LLM Summarization	Insights generated using llama3.2:1b via Ollama
Vector Store Logging	Summaries are stored in ChromaDB for persistent memory
Agentic Planning Tool	A dedicated LLM tool (autonomous_plan) determines next steps based on dataset context
Agentic Flow	LLM + memory + tool use + automatic reasoning + context awareness

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🧪 Tools Defined (via MCP)

Tool Name	Description	LLM Used
generate_data	Create synthetic tabular data (Gaussian + categorical)	❌
analyze_outliers	Label rows using Mahalanobis distance	❌
plot_results	Save a plot visualizing inliers vs outliers	❌
summarize_results	Interpret and explain outlier distribution using llama3.2:1b	✅
summarize_data_stats	Describe dataset trends using llama3.2:1b	✅
log_results_to_vector_store	Store summaries to ChromaDB for future reference	❌
search_logs	Retrieve relevant past sessions using vector search (optional LLM use)	⚠️
autonomous_plan	Run the full pipeline, use LLM to recommend next actions automatically	✅

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🤖 Agentic Capabilities

• Autonomy: LLM-guided execution path selection with autonomous_plan
• Tool Use: Dynamically invokes registered MCP tools via LLM inference
• Reasoning: Generates technical insights from dataset conditions and outlier analysis
• Memory: Persists and recalls knowledge using ChromaDB vector search
• LLM: Powered by Ollama with llama3.2:1b (temperature = 0.1, deterministic)

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🚀 Getting Started

1. Clone and Set Up

git clone https://github.com/MadMando/mcp-autonomous-analyst.git
cd mcp-autonomous-analyst
conda create -n mcp-agentic python=3.11 -y
conda activate mcp-agentic
pip install uv
uv pip install -r requirements.txt

2. Start the MCP Server

mcp run server.py --transport streamable-http

3. Start the Web Dashboard

uvicorn web:app --reload --port 8001

Then visit: http://localhost:8000

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🌐 Dashboard Flow

• Step 1: Upload your own dataset or click Generate Synthetic Data
• Step 2: The system runs anomaly detection on feature_1 vs feature_2
• Step 3: Visual plot of outliers is generated
• Step 4: Summaries are created via LLM
• Step 5: Results are optionally logged to vector store for recall

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📁 Project Layout

📦 autonomous-analyst/
├── server.py                  # MCP server
├── web.py                     # FastAPI + MCP client (frontend logic)
├── tools/
│   ├── synthetic_data.py
│   ├── outlier_detection.py
│   ├── plotter.py
│   ├── summarizer.py
│   ├── vector_store.py
├── static/                   # Saved plot
├── data/                     # Uploaded or generated dataset
├── requirements.txt
├── .gitignore
└── README.md

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📚 Tech Stack

• MCP SDK: mcp
• LLM Inference: Ollama running llama3.2:1b
• UI Server: FastAPI + Uvicorn
• Memory: ChromaDB vector database
• Data: pandas, matplotlib, scikit-learn

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✅ .gitignore Additions

__pycache__/
*.pyc
*.pkl
.env
static/
data/

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🙌 Acknowledgements

This project wouldn't be possible without the incredible work of the open-source community. Special thanks to:

Tool / Library	Purpose	Repository
🧠 Model Context Protocol (MCP)	Agentic tool orchestration & execution	modelcontextprotocol/python-sdk
💬 Ollama	Local LLM inference engine (llama3.2:1b)	ollama/ollama
🔍 ChromaDB	Vector database for logging and retrieval	chroma-core/chroma
🌐 FastAPI	Interactive, fast web interface	tiangolo/fastapi
⚡ Uvicorn	ASGI server powering the FastAPI backend	encode/uvicorn

💡 If you use this project, please consider starring or contributing to the upstream tools that make it possible.

This repo was created with the assistance of a local rag-llm using llama3.2:1b