MCP Autonomous Analyst

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)

⚙️ 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

🧪 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	✅

🤖 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)

🚀 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

🌐 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

📁 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

📚 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

✅ .gitignore Additions

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

🙌 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

This server cannot be installed

security - not tested

license - not found

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

A local, agentic AI pipeline that analyzes tabular data, detects anomalies, and generates interpretive summaries using local LLMs orchestrated via the Model Context Protocol.

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