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pytest-mcp-server

1.1.6 • Public • Published

Pytest MCP Server

A Model Context Protocol (MCP) server designed to help track pytest failures and resolve errors faster using the 9 principles of debugging.

Node.js Compatibility Tests

Overview

This MCP server provides tools for:

  1. Registering pytest failures
  2. Debugging failures using systematic debugging principles
  3. Tracking the debug progress
  4. Retrieving failure information
  5. Analyzing and grouping similar failures
  6. Generating targeted debugging prompts for LLMs

The server enables Large Language Models (LLMs) like Claude to systematically debug test failures using David Agans' 9 Rules of Debugging:

  1. Understand the System - Gain high-level awareness of the code and test behavior
  2. Make It Fail - Reproduce the failure consistently, eliminate flakiness
  3. Quit Thinking and Look - Examine actual runtime values and unexpected behavior
  4. Divide and Conquer - Narrow down where in the code the issue is occurring
  5. Change One Thing at a Time - Test isolated changes to validate hypotheses
  6. Keep an Audit Trail - Track changes and their effects during debugging
  7. Check the Plug - Verify basic configuration and environment issues
  8. Get a Fresh View - Approach the problem from a different perspective
  9. If You Didn't Fix It, It Ain't Fixed - Verify your solution works and addresses root causes

Installation

Prerequisites

  • Node.js 18+
  • npm or yarn
  • A pytest project to debug

Install the MCP Server

Option 1: Using npm (recommended)

# Install the package globally
npm install -g pytest-mcp-server

# Start the server
pytest-mcp-server start

Option 2: From Source

# Clone the repository
git clone https://github.com/tosin2013/pytest-mcp-server.git
cd pytest-mcp-server

# Install dependencies
npm install

# Build the project
npm run build

Running the MCP Server

When installed via npm

# Start the server with default settings
pytest-mcp-server start

# Use different ports for web UI and MCP transport
pytest-mcp-server start --port 8080 --mcp-port 8081

# Specify a custom data directory
pytest-mcp-server start --data-dir ./my-data --transport http-stream

# Or use environment variables
DATA_DIR=/path/to/data PORT=8080 MCP_PORT=8081 pytest-mcp-server start --transport http-stream

When installed from source

# Start the MCP server
npm start

# Start with environment variables
DATA_DIR=/path/to/data PORT=8080 MCP_PORT=8081 npm start

# Build and start the HTTP server with web UI
npm run build:all
npm run start-http

The web UI will be available at http://localhost:3000 by default, and the MCP API will run on port 3001 by default.

Environment Variables and Configuration

The pytest-mcp-server uses environment variables for configuration. You can view the current configuration using:

pytest-mcp-server check-env

This will display all environment variables used by the server and their current values.

Variable Description Default
PORT HTTP server port for web UI 3000
MCP_PORT Port for MCP transport server 3001
DATA_DIR Directory for storing failure data ./data in current directory

Port Configuration

The server uses two separate ports:

  1. Web UI Port (PORT environment variable, --port flag): Used for the web interface and HTTP API endpoints. Default: 3000
  2. MCP Transport Port (MCP_PORT environment variable, --mcp-port flag): Used for MCP protocol connections. Default: 3001

This separation ensures there are no port conflicts between the two servers. When configuring clients, make sure to use the correct port:

  • For web UI and REST API access: Use the Web UI port (e.g., http://localhost:3000)
  • For MCP protocol connections: Use the MCP Transport port (e.g., http://localhost:3001/mcp)

Data Directory Permissions

When configuring the data directory, ensure that:

  1. The user running the MCP server has read and write permissions for the data directory
  2. All parent directories have execute permissions to allow navigation to the data directory
  3. For Windsurf and Cursor, use a directory in your home folder to avoid permission issues

Recommended locations:

  • macOS/Linux: ~/pytest-data or another directory in your home folder
  • Windows: A directory in your user profile

Setting permissions (macOS/Linux):

# Create directory in home folder (recommended)
mkdir -p ~/pytest-data
chmod 755 ~/pytest-data

Avoid:

  • System directories that require elevated permissions
  • Network drives with unreliable connections
  • Directories with special characters in the path

You can check your current environment configuration by running:

# Check environment configuration
pytest-mcp-server check-env

# With environment variables
DATA_DIR=/path/to/data pytest-mcp-server check-env

This will display the current values of all environment variables used by the server.

Important Note on Data Directory

While the server attempts to create the data directory automatically, you might encounter permission issues on some systems. If you get an error related to directory creation, consider the following:

# Create the data directory manually
mkdir -p ./my-data

# Then start the server with that directory
DATA_DIR=./my-data pytest-mcp-server start

⚠️ Warning: Avoid using absolute paths at the root level (like /data) unless you have root permissions. This will likely cause permission errors like:

Error: ENOENT: no such file or directory, mkdir '/data'

Instead, use one of these approaches:

  1. Relative paths (recommended for most users):

    DATA_DIR=./my-data pytest-mcp-server start

  2. Home directory paths:

    DATA_DIR=$HOME/pytest-data pytest-mcp-server start

  3. Absolute paths with proper permissions:

    # Create directory with proper permissions first
sudo mkdir -p /var/lib/pytest-mcp
sudo chown $USER /var/lib/pytest-mcp

# Then use it
DATA_DIR=/var/lib/pytest-mcp pytest-mcp-server start

Always ensure the user running the server has write permissions to the specified directory.

Verifying Environment Variables

To verify that environment variables like DATA_DIR are correctly set, use the check-env command:

# Check environment configuration
pytest-mcp-server check-env

# With environment variables
DATA_DIR=/path/to/data pytest-mcp-server check-env

This will display the current values of all environment variables used by the server.

Using with Claude and Other AI Tools

Claude Desktop

To use the server with Claude Desktop, add the following to your Claude Desktop config file:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%/Claude/claude_desktop_config.json

Option 1: Using globally installed package

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "pytest-mcp-server",
      "args": ["start", "--transport", "http-stream"],
      "env": {
        "DATA_DIR": "/path/to/your/data/directory",
        "PORT": "3000",
        "MCP_PORT": "3001"
      }
    }
  }
}

Option 2: Using npx (no global installation)

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "npx",
      "args": ["pytest-mcp-server", "start", "--transport", "http-stream"],
      "env": {
        "DATA_DIR": "/path/to/your/data/directory",
        "PORT": "3000",
        "MCP_PORT": "3001"
      }
    }
  }
}

Configuration Options

You can customize the server configuration in several ways:

Setting a custom data directory with environment variables:

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "pytest-mcp-server",
      "args": ["start", "--transport", "http-stream"],
      "env": {
        "DATA_DIR": "/path/to/your/data/directory",
        "PORT": "3000",
        "MCP_PORT": "3001"
      }
    }
  }
}

Using command-line arguments:

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "pytest-mcp-server",
      "args": ["start", "--data-dir", "/path/to/your/data", "--port", "3000", "--mcp-port", "3001", "--transport", "http-stream"]
    }
  }
}

Running from a local repository clone:

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "node",
      "args": ["/absolute/path/to/pytest-mcp-server/dist/cli.js", "start", "--port", "3000", "--mcp-port", "3001", "--transport", "http-stream"],
      "env": {
        "DATA_DIR": "/path/to/your/data"
      }
    }
  }
}

In your IDE or Editor

To use with an IDE or Editor, add this to your configuration:

{
  "mcpServers": {
    "pytest-mcp-server": {
      "command": "npx",
      "args": ["-y", "pytest-mcp-server", "start", "--transport", "http-stream"],
      "env": {
        "DATA_DIR": "/path/to/your/data",
        "PORT": "3000",
        "MCP_PORT": "3001"
      }
    }
  }
}

Pytest Integration

To integrate with pytest, you'll need to install and configure the pytest-mcp plugin:

pip install pytest-mcp

Create a conftest.py file in your pytest project with the following content:

# conftest.py
import json
import requests
from pathlib import Path
import pytest
import traceback

def pytest_sessionstart(session):
    """Called after the Session object has been created and before tests are collected."""
    print("Starting test session with MCP integration")

def pytest_runtest_makereport(item, call):
    """Called when a test completes to build a report for the test."""
    if call.when == "call" and call.excinfo:
        # Only report when actual test calls fail
        tb_str = ''.join(traceback.format_exception(*call.excinfo._excinfo))
        
        # Extract locals from the frame where the test failed
        locals_dict = {}
        if call.excinfo.traceback:
            tb = call.excinfo.traceback[-1]  # Get the last frame in the traceback
            if hasattr(tb, 'locals'):
                locals_dict = tb.locals
        
        # Filter out non-serializable objects from locals
        filtered_locals = {}
        for key, value in locals_dict.items():
            try:
                # Try to serialize the value to test serializability
                json.dumps({key: str(value)})
                filtered_locals[key] = str(value)
            except (TypeError, OverflowError, ValueError):
                filtered_locals[key] = f"<non-serializable: {type(value).__name__}>"
        
        # Get source line number where the failure occurred
        line_number = item.function.__code__.co_firstlineno
        if call.excinfo.traceback:
            line_number = call.excinfo.traceback[-1].lineno
        
        # Prepare data to send to MCP server
        failure_data = {
            "test_name": item.name,
            "file_path": str(Path(item.fspath).resolve()),
            "line_number": line_number,
            "error_message": str(call.excinfo.value),
            "traceback": tb_str,
            "locals": filtered_locals
        }
        
        # Print debug info
        print("\n===== MCP Test Failure Registration =====")
        print(f"Test: {failure_data['test_name']}")
        print(f"File: {failure_data['file_path']}")
        print(f"Line: {failure_data['line_number']}")
        print(f"Error: {failure_data['error_message']}")
        
        # Send to MCP server - try both the HTTP API and MCP protocol
        endpoints = [
            "http://localhost:3000/api/failures",  # HTTP API endpoint (Web UI port)
            "http://localhost:3001/mcp/failures"   # MCP protocol endpoint (MCP transport port)
        ]
        
        success = False
        for endpoint in endpoints:
            try:
                response = requests.post(
                    endpoint, 
                    json=failure_data,
                    headers={"Content-Type": "application/json"},
                    timeout=5  # Set a timeout to avoid hanging
                )
                if response.status_code == 200:
                    result = response.json()
                    print(f"✅ Failure registered with MCP server at {endpoint}")
                    print(f"🔍 Failure ID: {result.get('failureId')}")
                    print(f"🔍 Session ID: {result.get('sessionId')}")
                    success = True
                    break
                else:
                    print(f"❌ Failed to register failure with MCP server at {endpoint}: {response.status_code}")
                    print(f"Response: {response.text}")
            except requests.RequestException as e:
                print(f"❌ Error connecting to MCP server at {endpoint}: {e}")
        
        if not success:
            print("⚠️ Could not register failure with any MCP server endpoint")

Using with Claude

Once you have failures registered with the MCP server, you can use Claude to debug them:

  1. Open Claude Desktop
  2. Ask questions like:
    • "I have a pytest failure. Can you help me debug it using the pytest-mcp-server?"
    • "List all the pytest failures in the server and help me debug the most recent one."
    • "Apply the 'Divide and Conquer' debugging principle to my latest test failure."
    • "Analyze my test failures and identify common patterns."
    • "Generate a detailed debug prompt for my TypeError failures."

Using the MCP Inspector

The MCP Inspector is a tool for inspecting MCP server connections and interactions:

# Install the MCP Inspector
npm install -g @modelcontextprotocol/inspector

# Connect to the MCP server (use the MCP_PORT, not the web UI port)
npx @modelcontextprotocol/inspector inspect http://localhost:3001/mcp

You can also specify different ports for the Inspector UI:

# Custom ports for the Inspector
npx @modelcontextprotocol/inspector inspect http://localhost:3001/mcp --port 4000 --ui-port 5000

Web UI Features

The pytest-mcp-server includes a web interface for visualizing and interacting with test failures:

Dashboard

  • Failure count by status (new, in-progress, resolved)
  • Recent failures list
  • Debugging progress charts

Architecture Documentation

To understand the architectural decisions and domain design of this project, explore our Architecture Decision Records (ADRs):

  1. MCP Tools Architecture - Core tool-based approach for implementing debugging capabilities
  2. JSON File-Based Storage - Data persistence strategy using JSON files
  3. Debugging Principles Process - Implementation of the 9 principles of debugging workflow
  4. Analytics and Failure Grouping - Approach for analyzing and grouping similar failures
  5. Multi-Interface Architecture - Design for supporting both MCP and HTTP interfaces
  6. Automated npm.js Package Publishing - Strategy for distributing the package via npm.js using GitHub Actions

Additional architecture documentation:

Failure Analytics

  • Group failures by error type, file path, or pattern
  • Filter by time ranges (today, week, month, all time)
  • Visualize error trends and most frequent failure locations
  • Auto-generated root cause hypotheses
  • Priority ranking of failures

Debugging Interface

  • Apply and track the 9 debugging principles
  • AI-assisted prompt generation
  • One-click application of common solutions
  • Debugging history timeline

MCP Tools

1. Register Pytest Failure

Registers a new test failure for debugging.

Parameters:

  • test_name: Name of the failing test
  • file_path: Path to the file containing the test
  • line_number: Line number of the failure
  • error_message: Error message from the test failure
  • traceback: Full traceback of the failure
  • locals: Local variables at the point of failure (optional)

2. Debug with Principle

Apply a debugging principle to a pytest failure and record the analysis.

Parameters:

  • failure_id: ID of the test failure to debug
  • principle_number: Number of the debugging principle to apply (1-9, optional)
  • analysis: Your analysis of the test failure using the specified debugging principle

3. Get Failure Info

Get information about a pytest failure and its debugging progress.

Parameters:

  • failure_id: ID of the test failure to get information about

4. List Failures

List all pytest failures with optional status filtering.

Parameters:

  • status: Optional status filter (e.g., 'new', 'in_progress', 'resolved')

5. Analyze Failures

Group and analyze similar test failures, generate insights, and automate triage.

Parameters:

  • group_by: Method to group failures (error_type, file_path, pattern)
  • time_range: Time range for analysis (all, today, week, month)
  • include_resolved: Whether to include resolved failures in the analysis

6. Generate Debug Prompt

Create targeted debugging prompts for LLM-based debugging.

Parameters:

  • group_id: ID of the failure group to generate a prompt for (optional)
  • failure_id: ID of a specific failure to generate a prompt for (optional)
  • prompt_style: Style of prompt to generate (detailed, concise, step_by_step, root_cause)

7. Get Documentation

Get information about using the pytest-mcp-server.

Parameters:

  • topic: Optional topic to get documentation for (general, integration, principles, api, client, webui, analytics, prompts)

Command-Line Interface

The pytest-mcp-server includes a Python client for interacting with the server:

# Register a test failure
python test-client.py --action register

# List all failures
python test-client.py --action list

# Get details about a failure
python test-client.py --action info --failure-id <failure_id>

# Apply a debugging principle
python test-client.py --action debug --failure-id <failure_id> --principle <1-9> --analysis "Your analysis..."

# Analyze failures and create groups
python test-client.py --action analyze --group-by error_type

# Generate a debug prompt
python test-client.py --action prompt --failure-id <failure_id> --style detailed

# Run a full demo
python test-client.py --action demo

Development

Project Structure

pytest-mcp-server/
├── src/
│   ├── tools/
│   │   ├── PytestFailureTool.ts        # Tool for registering failures
│   │   ├── DebugWithPrincipleTool.ts   # Tool for applying debugging principles
│   │   ├── GetFailureInfoTool.ts       # Tool for getting failure info
│   │   ├── ListFailuresTool.ts         # Tool for listing failures
│   │   ├── FailureAnalyticsTool.ts     # Tool for analyzing and grouping failures
│   │   ├── FailurePromptGeneratorTool.ts # Tool for generating debug prompts
│   │   └── PytestDocsGuideTool.ts      # Tool for providing documentation
│   ├── cli.ts                          # Command-line interface for npm package
│   ├── http-server.ts                  # HTTP API server
│   └── index.ts                        # Server entry point
├── .github/
│   └── workflows/
│       └── npm-publish.yml             # GitHub Actions workflow for npm publishing
├── data/                               # Data storage directory (created at runtime)
│   ├── failures.json                   # Stored failures
│   ├── debug_sessions.json             # Debug session data
│   └── failure_groups.json             # Failure grouping data
├── web-ui/                             # React-based web interface
│   ├── src/                            # Web UI source code
│   │   ├── components/                 # React components
│   │   ├── services/                   # API services
│   │   └── pages/                      # Page components
│   └── package.json                    # Web UI dependencies
├── test-client.py                      # Python test client
├── package.json                        # Server dependencies
└── tsconfig.json                       # TypeScript configuration

Publishing to npm.js

The project uses GitHub Actions to automate the npm publishing process. To release a new version:

  1. Update the version in package.json (following semantic versioning)
  2. Create and push your changes
  3. Create a new GitHub release with tag v{version} (e.g., v1.0.1)
  4. The GitHub Actions workflow will automatically publish the new version to npm.js

You can also manually trigger the workflow from the GitHub Actions tab.

Building Custom Tools

To add a new tool, create a new TypeScript file in the src/tools directory:

import { MCPTool } from "mcp-framework";
import { z } from "zod";

interface YourToolInput {
  param1: string;
  param2: number;
}

class YourTool extends MCPTool<YourToolInput> {
  name = "your_tool_name";
  description = "Description of your tool";

  schema = {
    param1: {
      type: z.string(),
      description: "Description of param1",
    },
    param2: {
      type: z.number(),
      description: "Description of param2",
    },
  };

  async execute(input: YourToolInput) {
    // Tool implementation
    return {
      result: "Your result here"
    };
  }
}

export default YourTool;

HTTP API Endpoints

The server provides a RESTful HTTP API for programmatic access on the Web UI port (default: 3000):

  • GET /api/failures - List all registered failures
  • POST /api/failures - Register a new test failure
  • GET /api/failures/:id - Get details about a specific failure
  • POST /api/debug - Apply a debugging principle to a failure
  • GET /api/analytics - Get analytics and grouping for test failures
  • GET /api/prompts/:id - Generate a debugging prompt for a failure or group
  • GET /api/docs - Get documentation about using the server

For MCP protocol connections, use the MCP Transport port (default: 3001) with the /mcp endpoint.

For full API documentation, visit the web UI at http://localhost:3000/docs (replace with your configured port).

License

MIT

Readme

Keywords

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Version

1.1.6

License

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  • tosin2013
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