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

uv add fastmcp numpy scipy pandas

uv sync

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

# Using uv
uv run python main.py

# Or if installed
python main.py

# 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")

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

# 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")

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

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

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

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

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

data_summary_from_csv_text(csv_text)

{
    "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
}

{
    "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
}

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

uv run mypy main.py

uv run pytest

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

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