jaggederest/mcp_reviewer

Reviewer MCP

An MCP (Model Context Protocol) service that provides AI-powered development workflow tools. It supports multiple AI providers (OpenAI and Ollama) and offers standardized tools for specification generation, code review, and project management.

Features

• Specification Generation: Create detailed technical specifications from prompts
• Specification Review: Review specifications for completeness and provide critical feedback
• Code Review: Analyze code changes with focus on security, performance, style, or logic
• Test Runner: Execute tests with LLM-friendly formatted output
• Linter: Run linters with structured output formatting
• Pluggable AI Providers: Support for both OpenAI and Ollama (local models)

Installation

npm install
npm run build

Configuration

Environment Variables

Create a .env file based on .env.example:

# AI Provider Configuration
AI_PROVIDER=openai  # Options: openai, ollama

# OpenAI Configuration
OPENAI_API_KEY=your_api_key_here
OPENAI_MODEL=o1-preview

# Ollama Configuration (for local models)
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama2

Project Configuration

Create a .reviewer.json file in your project root to customize commands:

{
  "testCommand": "npm test",
  "lintCommand": "npm run lint",
  "buildCommand": "npm run build",
  "aiProvider": "ollama",
  "ollamaModel": "codellama"
}

Using with Claude Desktop

Add the following to your Claude Desktop configuration:

{
  "mcpServers": {
    "reviewer": {
      "command": "node",
      "args": ["/path/to/reviewer-mcp/dist/index.js"],
      "env": {
        "OPENAI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Using with Ollama

1. Install Ollama: https://ollama.ai
2. Pull a model: ollama pull llama2 or ollama pull codellama
3. Set AI_PROVIDER=ollama in your .env file
4. The service will use your local Ollama instance

Available Tools

generate_spec

Generate a technical specification document.

Parameters:

• prompt (required): Description of what specification to generate
• context (optional): Additional context or requirements
• format (optional): Output format - "markdown" or "structured"

review_spec

Review a specification for completeness and provide critical feedback.

Parameters:

• spec (required): The specification document to review
• focusAreas (optional): Array of specific areas to focus the review on

review_code

Review code changes and provide feedback.

Parameters:

• diff (required): Git diff or code changes to review
• context (optional): Context about the changes
• reviewType (optional): Type of review - "security", "performance", "style", "logic", or "all"

run_tests

Run standardized tests for the project.

Parameters:

• testCommand (optional): Test command to run (defaults to configured command)
• pattern (optional): Test file pattern to match
• watch (optional): Run tests in watch mode

run_linter

Run standardized linter for the project.

Parameters:

• lintCommand (optional): Lint command to run (defaults to configured command)
• fix (optional): Attempt to fix issues automatically
• files (optional): Array of specific files to lint

Development

# Run in development mode
npm run dev

# Run tests
npm test

# Run unit tests only
npm run test:unit

# Run integration tests (requires Ollama)
npm run test:integration

# Type checking
npm run typecheck

# Linting
npm run lint

End-to-End Testing

The project includes a comprehensive e2e test that validates the full workflow using a real Ollama instance:

1. Install and start Ollama: https://ollama.ai
2. Pull a model: ollama pull llama2
3. Run the test: npm run test:e2e

The e2e test demonstrates:

• Specification generation
• Specification review
• Code creation
• Code review
• Linting
• Test execution

All using real AI responses from your local Ollama instance.

License

MIT