--- title: goose Docs Extension description: Add goose Docs MCP Server as a goose Extension --- import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import CLIExtensionInstructions from '@site/src/components/CLIExtensionInstructions'; import GooseDesktopInstaller from '@site/src/components/GooseDesktopInstaller'; This tutorial covers how to add the [goose Docs MCP Server](https://github.com/idosal/git-mcp) as a goose extension to enable goose to answer questions about itself. :::tip Quick Install [Launch the installer](goose://extension?cmd=npx&arg=mcp-remote&arg=https%3A%2F%2Fblock.gitmcp.io%2Fgoose%2F&id=goose-docs&name=goose%20Docs&description=gitmcp%20for%20goose%20documentation) **Command** ```sh npx mcp-remote https://block.gitmcp.io/goose/ ``` ::: ## Configuration ## Example Usage GitMCP allows you to convert any Git repository into a goose extension. The goose Docs MCP Server is a great example of this, as it provides a way for goose to answer questions about itself. Let's ask goose to create a recipe for us! It will use the goose Docs MCP Server to find the relevant information. ### goose Prompt ``` hey goose, I want to create a recipe for automated code review that my development team can use. Can you look at the goose documentation to understand the recipe format, then create a recipe that: 1. Reviews pull requests for: - Code quality issues and best practices - Security vulnerabilities - Missing unit tests - Performance concerns 2. Should be parameterized so teams can customize: - Programming language (Python, JavaScript, etc.) - Minimum test coverage threshold - Specific coding standards to enforce 3. Should output structured results that can be parsed by CI/CD systems Please research the recipe format first using the goose Docs extension, then create a complete recipe file with proper YAML structure, parameters, and any needed extensions. Make it something a development team could immediately use in their workflow. ``` ### goose Output :::note Desktop Perfect! I've created a comprehensive automated code review recipe based on the goose documentation research. Here's what I've built for your development team: #### 📋 Recipe Overview The **Automated Code Review** recipe (`automated-code-review.yaml`) provides: #### ✅ **Core Review Areas** 1. **Code Quality & Best Practices** - Architecture, naming, complexity, documentation 2. **Security Vulnerability Assessment** - OWASP Top 10, input validation, auth issues 3. **Test Coverage Analysis** - Missing tests, coverage thresholds, test quality 4. **Performance Concerns** - Bottlenecks, algorithm complexity, resource management 5. **Technical Debt Assessment** - Refactoring needs, maintainability issues #### 🔧 **Parameterization Features** - **`programming_language`** - Python, JavaScript, Java, Go, Rust, TypeScript, etc. - **`min_test_coverage`** - Customizable threshold (default: 80%) - **`coding_standards`** - PEP8, ESLint, Google Style Guide, company-specific - **`security_focus`** - Basic, standard, or comprehensive security analysis - **`performance_analysis`** - Enable/disable performance review - **`exclude_patterns`** - Skip certain files/directories #### 📊 **Structured CI/CD Output** The recipe outputs a comprehensive JSON structure with: - **Overall quality score** and recommendation (approve/request changes/reject) - **Categorized issues** with severity levels and specific file locations - **Security vulnerabilities** with CWE IDs and remediation steps - **Test coverage metrics** and missing test recommendations - **Performance concerns** with optimization suggestions - **Technical debt assessment** with effort estimates - **CI/CD metadata** for pipeline integration #### 🚀 **Usage Examples** #### Basic Usage: ```bash goose run automated-code-review.yaml \ --repository_path ./my-project \ --programming_language Python ``` #### Advanced Usage: ```bash goose run automated-code-review.yaml \ --repository_path ./my-project \ --programming_language JavaScript \ --min_test_coverage 85 \ --coding_standards ESLint \ --security_focus comprehensive \ --exclude_patterns "*.test.js,dist/*,node_modules/*" ``` #### 🔧 **CI/CD Integration** The structured JSON output makes it perfect for CI/CD pipelines: ```yaml # Example GitHub Actions integration - name: Run Code Review run: | goose run automated-code-review.yaml \ --repository_path . \ --programming_language ${{ matrix.language }} \ --output review-results.json - name: Process Results run: | # Parse JSON results for pipeline decisions RECOMMENDATION=$(jq -r '.review_summary.recommendation' review-results.json) if [ "$RECOMMENDATION" = "reject" ]; then exit 1 fi ``` #### 📁 **File Structure** The recipe is saved as `automated-code-review.yaml` in your current directory and includes: - Proper YAML structure following goose recipe format - Comprehensive parameter definitions with defaults - Built-in developer extension for file system access - Detailed JSON schema for structured output - Template syntax for parameter substitution Your development team can immediately start using this recipe by placing it in their project repositories and customizing the parameters for their specific needs! ::: ### Generated Recipe File

automated-code-review.yaml

```yaml version: "1.0.0" title: "Automated Code Review" description: "Comprehensive automated code review for pull requests with customizable standards and structured CI/CD output" instructions: | You are an expert code reviewer with deep knowledge of software engineering best practices, security vulnerabilities, and testing methodologies. Your role is to perform a thorough code review focusing on: 1. Code Quality & Best Practices 2. Security Vulnerabilities 3. Test Coverage Analysis 4. Performance Concerns 5. Maintainability Issues Always provide actionable feedback with specific examples and suggestions for improvement. Consider the specified programming language conventions and coding standards. parameters: - key: repository_path input_type: string requirement: required description: "Path to the repository or pull request to review" - key: programming_language input_type: string requirement: required description: "Primary programming language (e.g., Python, JavaScript, Java, Go, Rust, TypeScript)" - key: min_test_coverage input_type: number requirement: optional default: 80 description: "Minimum test coverage threshold percentage (0-100)" - key: coding_standards input_type: string requirement: optional default: "industry_standard" description: "Coding standards to enforce (e.g., PEP8, ESLint, Google Style Guide, company_specific)" - key: security_focus input_type: string requirement: optional default: "standard" description: "Security review depth (basic, standard, comprehensive)" - key: performance_analysis input_type: string requirement: optional default: "true" description: "Enable performance analysis (true/false)" - key: exclude_patterns input_type: string requirement: optional default: "*.md,*.txt,*.json,node_modules/*,vendor/*,.git/*" description: "Comma-separated patterns of files/directories to exclude from review" extensions: - type: builtin name: developer timeout: 600 bundled: true description: "Access to file system and shell commands for code analysis" prompt: | Perform a comprehensive code review of the {{ programming_language }} codebase at {{ repository_path }}. ## Review Scope - Programming Language: {{ programming_language }} - Minimum Test Coverage: {{ min_test_coverage }}% - Coding Standards: {{ coding_standards }} - Security Focus: {{ security_focus }} - Performance Analysis: {{ performance_analysis }} - Exclude Patterns: {{ exclude_patterns }} ## Analysis Requirements ### 1. Code Quality & Best Practices - Review code structure, organization, and architecture - Check adherence to {{ coding_standards }} standards - Identify code smells, anti-patterns, and technical debt - Evaluate naming conventions, documentation, and readability - Check for proper error handling and logging ### 2. Security Vulnerability Assessment {% if security_focus == "comprehensive" %} Perform comprehensive security analysis including: - OWASP Top 10 vulnerabilities - Input validation and sanitization - Authentication and authorization flaws - Cryptographic implementations - Dependency vulnerabilities - Information disclosure risks {% elif security_focus == "standard" %} Perform standard security review: - Common vulnerability patterns - Input validation issues - Authentication/authorization problems - Basic cryptographic concerns {% else %} Perform basic security check: - Obvious security anti-patterns - Hardcoded credentials - Basic input validation {% endif %} ### 3. Test Coverage Analysis - Analyze existing test files and coverage - Identify missing unit tests for critical functions - Check for integration and end-to-end test gaps - Evaluate test quality and maintainability - Verify test coverage meets {{ min_test_coverage }}% threshold ### 4. Performance Concerns {% if performance_analysis == "true" %} - Identify potential performance bottlenecks - Review algorithm complexity and efficiency - Check for memory leaks and resource management - Analyze database queries and data access patterns - Review caching strategies and optimization opportunities {% endif %} ### 5. Maintainability & Technical Debt - Assess code complexity and maintainability - Identify areas needing refactoring - Check for proper separation of concerns - Evaluate dependency management - Review documentation completeness ## Output Requirements Provide structured analysis that can be parsed by CI/CD systems, including: - Overall quality score and recommendation - Categorized issues with severity levels - Specific file locations and line numbers - Actionable remediation steps - Test coverage metrics and missing test recommendations - Security findings with risk assessments response: json_schema: type: object properties: review_summary: type: object properties: overall_score: type: number minimum: 0 maximum: 100 description: "Overall code quality score (0-100)" recommendation: type: string enum: ["approve", "approve_with_suggestions", "request_changes", "reject"] description: "Review recommendation for CI/CD pipeline" total_issues: type: number description: "Total number of issues found" critical_issues: type: number description: "Number of critical/blocking issues" required: ["overall_score", "recommendation", "total_issues", "critical_issues"] code_quality: type: object properties: score: type: number minimum: 0 maximum: 100 description: "Code quality score" issues: type: array items: type: object properties: severity: type: string enum: ["critical", "major", "minor", "info"] category: type: string enum: ["architecture", "naming", "complexity", "documentation", "best_practices"] file_path: type: string description: "Relative path to the file" line_number: type: number description: "Line number where issue occurs" description: type: string description: "Description of the issue" suggestion: type: string description: "Suggested fix or improvement" required: ["severity", "category", "file_path", "description", "suggestion"] required: ["score", "issues"] security_analysis: type: object properties: risk_score: type: number minimum: 0 maximum: 100 description: "Security risk score (0=low risk, 100=high risk)" vulnerabilities: type: array items: type: object properties: severity: type: string enum: ["critical", "high", "medium", "low"] type: type: string description: "Type of vulnerability (e.g., SQL Injection, XSS, etc.)" file_path: type: string line_number: type: number description: type: string impact: type: string description: "Potential impact of the vulnerability" remediation: type: string description: "Steps to fix the vulnerability" cwe_id: type: string description: "Common Weakness Enumeration ID if applicable" required: ["severity", "type", "file_path", "description", "impact", "remediation"] required: ["risk_score", "vulnerabilities"] test_coverage: type: object properties: current_coverage: type: number minimum: 0 maximum: 100 description: "Current test coverage percentage" meets_threshold: type: boolean description: "Whether coverage meets the minimum threshold" missing_tests: type: array items: type: object properties: file_path: type: string function_name: type: string line_number: type: number priority: type: string enum: ["critical", "high", "medium", "low"] test_type: type: string enum: ["unit", "integration", "end_to_end"] description: type: string description: "Description of what should be tested" required: ["file_path", "function_name", "priority", "test_type", "description"] test_quality_issues: type: array items: type: object properties: file_path: type: string issue: type: string suggestion: type: string required: ["file_path", "issue", "suggestion"] required: ["current_coverage", "meets_threshold", "missing_tests", "test_quality_issues"] performance_analysis: type: object properties: concerns: type: array items: type: object properties: severity: type: string enum: ["critical", "major", "minor"] type: type: string enum: ["algorithm_complexity", "memory_leak", "database_query", "resource_management", "caching"] file_path: type: string line_number: type: number description: type: string impact: type: string description: "Performance impact description" optimization: type: string description: "Suggested optimization" required: ["severity", "type", "file_path", "description", "impact", "optimization"] required: ["concerns"] technical_debt: type: object properties: debt_score: type: number minimum: 0 maximum: 100 description: "Technical debt score (0=low debt, 100=high debt)" refactoring_suggestions: type: array items: type: object properties: priority: type: string enum: ["critical", "high", "medium", "low"] area: type: string enum: ["architecture", "code_duplication", "complexity", "dependencies", "documentation"] file_path: type: string description: type: string effort_estimate: type: string enum: ["small", "medium", "large"] business_impact: type: string required: ["priority", "area", "file_path", "description", "effort_estimate"] required: ["debt_score", "refactoring_suggestions"] ci_cd_metadata: type: object properties: review_timestamp: type: string description: "ISO timestamp of when review was performed" reviewer: type: string description: "goose automated code review" language: type: string description: "Programming language reviewed" standards_applied: type: string description: "Coding standards that were applied" files_reviewed: type: number description: "Number of files reviewed" lines_of_code: type: number description: "Total lines of code reviewed" required: ["review_timestamp", "reviewer", "language", "standards_applied", "files_reviewed"] required: [ "review_summary", "code_quality", "security_analysis", "test_coverage", "performance_analysis", "technical_debt", "ci_cd_metadata" ] ```