feat: add MCP prompt templates for PyPI package analysis and decision-making
- Add comprehensive prompt templates for package analysis, dependency management, and migration planning - Implement 8 prompt templates covering quality analysis, package comparison, alternatives suggestion, dependency conflicts, version upgrades, security audits, and migration planning - Add detailed documentation in PROMPT_TEMPLATES.md with usage examples - Include demo script and test coverage for prompt template functionality - Update README.md to highlight new prompt template features - Templates provide structured guidance for common PyPI package scenarios Signed-off-by: longhao <hal.long@outlook.com>
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PROMPT_TEMPLATES.md
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PROMPT_TEMPLATES.md
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# PyPI Query MCP Server - Prompt Templates
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This document describes the MCP prompt templates available in the PyPI Query MCP Server. These templates provide structured guidance for common PyPI package analysis, dependency management, and migration scenarios.
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## 🎯 Overview
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Prompt templates are reusable message templates that help you get structured guidance from LLMs for specific PyPI package management tasks. They provide comprehensive frameworks for analysis and decision-making.
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## 📋 Available Prompt Templates
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### Package Analysis Templates
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#### 1. `analyze_package_quality`
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Generate a comprehensive quality analysis prompt for a PyPI package.
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**Parameters:**
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- `package_name` (required): Name of the PyPI package to analyze
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- `version` (optional): Specific version to analyze
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**Use Case:** When you need to evaluate a package's quality, maintenance status, security, and suitability for your project.
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**Example:**
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```json
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{
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"package_name": "requests",
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"version": "2.31.0"
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}
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```
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#### 2. `compare_packages`
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Generate a detailed comparison prompt for multiple PyPI packages.
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**Parameters:**
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- `packages` (required): List of package names to compare (2-5 packages)
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- `use_case` (required): Specific use case or project context
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- `criteria` (optional): Specific criteria to focus on
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**Use Case:** When choosing between multiple packages that serve similar purposes.
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**Example:**
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```json
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{
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"packages": ["requests", "httpx", "aiohttp"],
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"use_case": "Building a high-performance web API client",
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"criteria": ["performance", "async support", "ease of use"]
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}
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```
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#### 3. `suggest_alternatives`
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Generate a prompt for finding package alternatives.
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**Parameters:**
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- `package_name` (required): Package to find alternatives for
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- `reason` (required): Reason for seeking alternatives (deprecated, security, performance, licensing, maintenance, features)
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- `requirements` (optional): Specific requirements for alternatives
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**Use Case:** When you need to replace a package due to specific concerns.
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**Example:**
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```json
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{
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"package_name": "flask",
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"reason": "performance",
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"requirements": "Need async support and better performance"
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}
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```
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### Dependency Management Templates
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#### 4. `resolve_dependency_conflicts`
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Generate a prompt for resolving dependency conflicts.
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**Parameters:**
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- `conflicts` (required): List of conflicting dependencies or error messages
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- `python_version` (optional): Target Python version
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- `project_context` (optional): Brief project description
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**Use Case:** When facing dependency version conflicts that need resolution.
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**Example:**
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```json
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{
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"conflicts": [
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"django 4.2.0 requires sqlparse>=0.3.1, but you have sqlparse 0.2.4"
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],
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"python_version": "3.10",
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"project_context": "Django web application"
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}
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```
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#### 5. `plan_version_upgrade`
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Generate a prompt for planning package version upgrades.
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**Parameters:**
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- `package_name` (required): Package to upgrade
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- `current_version` (required): Current version being used
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- `target_version` (optional): Target version or 'latest'
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- `project_size` (optional): Project size context (small/medium/large/enterprise)
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**Use Case:** When planning major version upgrades that might have breaking changes.
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**Example:**
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```json
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{
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"package_name": "django",
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"current_version": "3.2.0",
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"target_version": "4.2.0",
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"project_size": "large"
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}
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```
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#### 6. `audit_security_risks`
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Generate a prompt for security risk auditing of packages.
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**Parameters:**
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- `packages` (required): List of packages to audit
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- `environment` (optional): Environment context (development/staging/production)
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- `compliance_requirements` (optional): Specific compliance requirements
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**Use Case:** When conducting security audits or compliance assessments.
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**Example:**
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```json
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{
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"packages": ["django", "requests", "pillow"],
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"environment": "production",
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"compliance_requirements": "SOC2, GDPR compliance"
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}
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```
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### Migration Planning Templates
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#### 7. `plan_package_migration`
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Generate a comprehensive package migration plan prompt.
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**Parameters:**
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- `from_package` (required): Package to migrate from
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- `to_package` (required): Package to migrate to
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- `codebase_size` (optional): Size of codebase (small/medium/large/enterprise)
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- `timeline` (optional): Desired timeline
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- `team_size` (optional): Number of developers involved
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**Use Case:** When planning to migrate from one package to another.
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**Example:**
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```json
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{
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"from_package": "flask",
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"to_package": "fastapi",
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"codebase_size": "medium",
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"timeline": "2 months",
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"team_size": 4
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}
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```
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#### 8. `generate_migration_checklist`
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Generate a detailed migration checklist prompt.
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**Parameters:**
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- `migration_type` (required): Type of migration (package_replacement, version_upgrade, framework_migration, dependency_cleanup)
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- `packages_involved` (required): List of packages involved
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- `environment` (optional): Target environment (development/staging/production/all)
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**Use Case:** When you need a comprehensive checklist for migration tasks.
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**Example:**
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```json
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{
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"migration_type": "package_replacement",
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"packages_involved": ["flask", "fastapi"],
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"environment": "production"
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}
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```
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## 🚀 Usage Examples
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### In Claude Desktop
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Add the PyPI Query MCP Server to your Claude Desktop configuration, then use prompts like:
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```
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Use the "analyze_package_quality" prompt template to analyze the requests package version 2.31.0
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```
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### In Cursor
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Configure the MCP server in Cursor, then access prompts through the command palette or by typing:
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```
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@pypi-query analyze_package_quality requests 2.31.0
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```
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### Programmatic Usage
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```python
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from fastmcp import Client
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async def use_prompt_template():
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client = Client("pypi_query_mcp.server:mcp")
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async with client:
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# Get a prompt template
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result = await client.get_prompt(
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"analyze_package_quality",
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{"package_name": "requests", "version": "2.31.0"}
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)
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# The result contains structured messages for the LLM
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print(result.messages[0].content.text)
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```
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## 🎨 Customization
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The prompt templates are designed to be comprehensive but can be customized by:
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1. **Modifying parameters**: Adjust the input parameters to focus on specific aspects
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2. **Combining templates**: Use multiple templates for complex scenarios
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3. **Extending context**: Add project-specific context through optional parameters
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## 🔧 Development
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To add new prompt templates:
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1. Create the template function in the appropriate module under `pypi_query_mcp/prompts/`
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2. Register it in `pypi_query_mcp/server.py` using the `@mcp.prompt()` decorator
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3. Add it to the `__all__` list in `pypi_query_mcp/prompts/__init__.py`
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4. Update this documentation
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## 📚 Best Practices
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1. **Be Specific**: Provide detailed context in the parameters for better results
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2. **Use Appropriate Templates**: Choose the template that best matches your scenario
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3. **Combine with Tools**: Use prompt templates alongside the MCP tools for comprehensive analysis
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4. **Iterate**: Refine your parameters based on the LLM responses to get better guidance
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## 🤝 Contributing
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We welcome contributions to improve existing templates or add new ones. Please:
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1. Follow the existing template structure and patterns
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2. Include comprehensive parameter validation
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3. Add examples and documentation
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4. Test with various scenarios
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## 📄 License
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These prompt templates are part of the PyPI Query MCP Server and are licensed under the same terms.
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