MCP Data Analysis Agent

MCP Data Analysis Agent

An MCP server that answers natural-language questions over CSV, Excel, and SQL data by providing deterministic tools for loading, profiling, querying, cleaning, statistical analysis, visualization, and reporting. It enables LLMs to plan and interpret while all computation is done exactly through MCP tools.

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README

📊 Autonomous Data Analysis System (MCP)

An AI data analyst that answers natural-language questions over CSV / Excel / SQL data. The language model plans and interprets — all computation (SQL, stats, cleaning, charts) runs in deterministic MCP tools, so results are exact, reproducible, and auditable.

Core principle: the LLM never does arithmetic. It reads schemas, chooses which tool to call with which arguments, and turns the returned numbers into business insight.

Architecture

┌──────────────────────────────────────────────────────────┐
│  Streamlit UI   (upload · chat · charts · report · switch) │
└───────────────┬──────────────────────────────────────────┘
                │
┌───────────────▼──────────────────────────────────────────┐
│  Agent host                                                │
│   • Provider switch (LiteLLM): Ollama · Groq · Gemini      │
│   • MCP client: MCP tool schemas ⇆ OpenAI function calls   │
│   • Loop: plan → call tool → observe → answer              │
└───────────────┬──────────────────────────────────────────┘
                │ MCP protocol (stdio)
┌───────────────▼──────────────────────────────────────────┐
│  MCP analysis server                                       │
│   load · profile · sql · eda · clean · stats · viz · report│
│   Dataset registry · Pandas · DuckDB · SciPy · matplotlib  │
└──────────────────────────────────────────────────────────┘

See DESIGN.md for the full design and milestone tracker.

Features

  • Upload CSV / Excel; DuckDB SQL over your files with no database server.
  • Automated profiling & EDA — schema, nulls, cardinality, correlations, group aggregates.
  • Non-destructive cleaning — missing values, duplicates, type casts, outliers (each returns a new versioned dataset).
  • Statistics — t-test / ANOVA / chi-square, correlation tests, trend analysis, distribution/normality.
  • Charts — bar, line, scatter, histogram, box, correlation heatmap (PNG).
  • Self-contained HTML reports with embedded charts.
  • Pluggable LLM — local Ollama by default, switch to Groq or Gemini free tiers.

Setup

python3 -m venv .venv
source .venv/bin/activate          # fish: source .venv/bin/activate.fish
pip install -r requirements.txt
cp .env.example .env               # then fill in the provider you want

Choosing a provider (edit .env)

Provider Setup Notes
Ollama (default, local) ollama pull qwen2.5:3b and run the Ollama daemon Free & private; smaller models plan tools less reliably
Groq (free tier) GROQ_API_KEY=... from https://console.groq.com/keys Fast, strong tool-calling — best for reliable planning
Gemini (free tier) GEMINI_API_KEY=... from https://aistudio.google.com/app/apikey Large context, strong function-calling

You can also switch provider live from the sidebar dropdown in the UI.

Run the app

streamlit run ui/streamlit_app.py

Then upload one of the samples in data/ (e.g. sample_sales.csv) and ask things like "Which region sells the most units, and is the trend rising?" or "Which plan has the highest churn — build me a report."

Use the MCP server directly (e.g. Claude Desktop)

The server speaks stdio and works with any MCP client:

python -m mcp_server.server

Claude Desktop config:

{ "mcpServers": {
    "data-analysis": { "command": "python", "args": ["-m", "mcp_server.server"] }
} }

MCP tool catalog

Group Tools
Load load_csv, load_excel, list_datasets
Profile profile_dataset
SQL run_sql (DuckDB)
EDA value_counts, correlations, groupby_aggregate
Clean drop_duplicates, handle_missing, cast_types, handle_outliers, rename_columns
Stats hypothesis_test, correlation_test, trend_analysis, distribution_fit
Viz make_chart
Report export_report

Testing

PYTHONPATH="$PWD" pytest -q

15 tests spawn the real MCP server over stdio and exercise every tool group; the agent loop is tested with a scripted fake LLM (deterministic, no network).

Project structure

mcp_server/     FastMCP server + registry + tools/
agent/          provider switch (config, providers) + mcp_client + agent loop
ui/             streamlit_app.py
data/           sample datasets + uploads/
reports/        generated charts and HTML reports
tests/          per-milestone test suites

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