Refactor application structure by removing separate server scripts and integrating functionality into app.py; add independent two-sample t-test and Pearson correlation coefficient tools using SciPy.

Dockerfile

@@ -1,14 +1,17 @@
-# Use an official Python runtime as a parent image
 FROM python:3.12-slim
 
-# Set the working directory in the container
+# Set the working directory
 WORKDIR /app
 
-# Copy the current directory contents into the container at /app
-COPY . /app
+# Copy the requirements file and install dependencies
+COPY ./requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r /app/requirements.txt
 
-# Install any needed packages specified in requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
+# Copy the application code
+COPY ./app.py /app/app.py
 
-# Run main.py to start the FastAPI server
-CMD ["python", "main.py"]
+# Expose the port the app runs on
+EXPOSE 7860
+
+# Command to run the application
+CMD ["python", "/app/app.py"]

app.py

@@ -2,6 +2,7 @@ from fastmcp import FastMCP
 import numpy as np
 from pydantic import BaseModel
 from typing import List, Tuple, Optional
+from scipy import stats
 
 mcp = FastMCP("Demo 🚀")
 
@@ -228,5 +229,25 @@ def summarize_request(input: SummarizeRequestInput) -> str:
     """Generate a prompt asking for a summary."""
     return f"Please summarize the following text:\n\n{input.text}"
 
+class ScipyTtestInput(BaseModel):
+    a: List[float]
+    b: List[float]
+
+@mcp.tool()
+def scipy_ttest(input: ScipyTtestInput) -> dict:
+    """Performs an independent two-sample t-test."""
+    t_stat, p_value = stats.ttest_ind(input.a, input.b)
+    return {"t_statistic": t_stat, "p_value": p_value}
+
+class ScipyPearsonrInput(BaseModel):
+    x: List[float]
+    y: List[float]
+
+@mcp.tool()
+def scipy_pearsonr(input: ScipyPearsonrInput) -> dict:
+    """Calculates the Pearson correlation coefficient."""
+    corr_coefficient, p_value = stats.pearsonr(input.x, input.y)
+    return {"correlation_coefficient": corr_coefficient, "p_value": p_value}
+
 if __name__ == "__main__":
     mcp.run(transport="sse", host="0.0.0.0", port=7860)

main.py

@@ -1,18 +0,0 @@
-import os
-import uvicorn
-from fastapi import FastAPI
-
-# Import the FastMCP instances from numpy_server and scipy_server
-from numpy_server import mcp as numpy_mcp
-from scipy_server import mcp as scipy_mcp
-
-# Main FastAPI instance
-app = FastAPI()
-
-# Mount the sub-applications on different paths
-app.mount("/numpy", numpy_mcp.app)
-app.mount("/scipy", scipy_mcp.app)
-
-if __name__ == "__main__":
-    port = int(os.environ.get("PORT", 7860))
-    uvicorn.run("main:app", host="0.0.0.0", port=port, reload=True) 

numpy_server.py

@@ -1,17 +0,0 @@
-from fastmcp import FastMCP
-import numpy as np
-
-mcp = FastMCP()
-
-@mcp.tool()
-def numpy_add(a: list[float], b: list[float]) -> list[float]:
-    """Adds two vectors element-wise."""
-    return np.add(a, b).tolist()
-
-@mcp.tool()
-def numpy_multiply(a: list[float], b: list[float]) -> list[float]:
-    """Multiplies two vectors element-wise."""
-    return np.multiply(a, b).tolist()
-
-if __name__ == "__main__":
-    mcp.run() 

requirements.txt

@@ -1,5 +1,3 @@
 fastmcp
 numpy
 scipy
-fastapi
-uvicorn

scipy_server.py

@@ -1,19 +0,0 @@
-from fastmcp import FastMCP
-from scipy import stats
-
-mcp = FastMCP()
-
-@mcp.tool()
-def scipy_ttest(a: list[float], b: list[float]) -> dict:
-    """Performs an independent two-sample t-test."""
-    t_stat, p_value = stats.ttest_ind(a, b)
-    return {"t_statistic": t_stat, "p_value": p_value}
-
-@mcp.tool()
-def scipy_pearsonr(x: list[float], y: list[float]) -> dict:
-    """Calculates the Pearson correlation coefficient."""
-    corr_coefficient, p_value = stats.pearsonr(x, y)
-    return {"correlation_coefficient": corr_coefficient, "p_value": p_value}
-
-if __name__ == "__main__":
-    mcp.run()