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_1vsfeature_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