FastMCP Data Analysis Server

A Model Context Protocol (MCP) server that provides comprehensive data analysis utilities including statistical functions, probability distributions, and data processing tools.

Features

Probability Distributions

• Poisson Probability: Calculate point, cumulative, and survival probabilities
• Normal Distribution: PDF, CDF, and survival function calculations
• Binomial Probability: Complete binomial distribution analysis

Statistical Analysis

• Descriptive Statistics: Mean, median, mode, variance, skewness, kurtosis, quartiles
• Correlation Analysis: Pearson and Spearman correlation with significance testing
• Hypothesis Testing: One-sample t-tests with detailed results
• Linear Regression: Simple linear regression with R², MSE, and equation

Data Processing

• CSV Analysis: Process CSV text data and generate comprehensive summaries
• Data Summarization: Automatic detection of numeric/categorical columns

Installation

1. Initialize the project with uv:

uv init fastmcp-data-analysis-server
cd fastmcp-data-analysis-server

2. Install dependencies:

uv add fastmcp numpy scipy pandas

Or install from the pyproject.toml:

uv sync

3. Install development dependencies (optional):

uv add --dev pytest pytest-asyncio black isort mypy

Usage

Running the Server

# Using uv
uv run python main.py

# Or if installed
python main.py

Available Tools

1. Poisson Probability

# Point probability: P(X = k)
poisson_probability(lam=3.5, k=2, prob_type="point")

# Cumulative probability: P(X ≤ k)  
poisson_probability(lam=3.5, k=5, prob_type="cumulative")

# Survival probability: P(X > k)
poisson_probability(lam=3.5, k=4, prob_type="survival")

2. Descriptive Statistics

descriptive_statistics([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

3. Normal Distribution

# Standard normal
normal_probability(x=1.96, mean=0, std_dev=1, prob_type="cumulative")

# Custom normal distribution
normal_probability(x=85, mean=100, std_dev=15, prob_type="point")

4. Correlation Analysis

correlation_analysis(
    x_data=[1, 2, 3, 4, 5], 
    y_data=[2, 4, 6, 8, 10]
)

5. Hypothesis Testing

hypothesis_test_ttest(
    sample_data=[12, 15, 18, 16, 17], 
    population_mean=14, 
    alpha=0.05
)

6. Linear Regression

linear_regression_analysis(
    x_data=[1, 2, 3, 4, 5],
    y_data=[2, 4, 5, 4, 5]
)

7. Binomial Probability

# Probability of exactly 3 successes in 10 trials
binomial_probability(n=10, k=3, p=0.4, prob_type="point")

8. CSV Data Analysis

csv_text = """name,age,score
Alice,25,85
Bob,30,92
Charlie,22,78"""

data_summary_from_csv_text(csv_text)

Example Responses

Poisson Probability Response

{
    "probability": 0.2138,
    "description": "P(X = 2)",
    "lambda": 3.5,
    "k": 2,
    "prob_type": "point",
    "mean": 3.5,
    "variance": 3.5,
    "std_dev": 1.8708
}

Descriptive Statistics Response

{
    "count": 10,
    "mean": 5.5,
    "median": 5.5,
    "std_dev": 3.0277,
    "variance": 9.1667,
    "min": 1.0,
    "max": 10.0,
    "skewness": 0.0,
    "kurtosis": -1.2
}

Development

Code Formatting

uv run black main.py
uv run isort main.py

Type Checking

uv run mypy main.py

Testing

uv run pytest

MCP Client Integration

This server can be used with any MCP client. The tools are automatically exposed and can be called with the appropriate parameters.

Example MCP Client Usage

# Assuming you have an MCP client connected
client.call_tool("poisson_probability", {
    "lam": 2.5,
    "k": 3,
    "prob_type": "cumulative"
})

Example MCP Server Config

{
  "mcpServers": {
    "analysis-mcp": {
      "command": "fastmcp-data-analysis-server/.venv/bin/python",
      "args": [
        "fastmcp-data-analysis-server/main.py"
      ],
    }
  }
}

Error Handling

All functions include comprehensive error handling for:

• Invalid parameter values
• Empty datasets
• Mismatched data lengths
• Invalid probability types
• Mathematical domain errors

License

MIT License