python-mcp-server-generator
Generate a complete MCP server project in Python with tools, resources, and proper configuration
What this skill does
# Generate Python MCP Server
Create a complete Model Context Protocol (MCP) server in Python with the following specifications:
## Requirements
1. **Project Structure**: Create a new Python project with proper structure using uv
2. **Dependencies**: Include mcp[cli] package with uv
3. **Transport Type**: Choose between stdio (for local) or streamable-http (for remote)
4. **Tools**: Create at least one useful tool with proper type hints
5. **Error Handling**: Include comprehensive error handling and validation
## Implementation Details
### Project Setup
- Initialize with `uv init project-name`
- Add MCP SDK: `uv add "mcp[cli]"`
- Create main server file (e.g., `server.py`)
- Add `.gitignore` for Python projects
- Configure for direct execution with `if __name__ == "__main__"`
### Server Configuration
- Use `FastMCP` class from `mcp.server.fastmcp`
- Set server name and optional instructions
- Choose transport: stdio (default) or streamable-http
- For HTTP: optionally configure host, port, and stateless mode
### Tool Implementation
- Use `@mcp.tool()` decorator on functions
- Always include type hints - they generate schemas automatically
- Write clear docstrings - they become tool descriptions
- Use Pydantic models or TypedDicts for structured outputs
- Support async operations for I/O-bound tasks
- Include proper error handling
### Resource/Prompt Setup (Optional)
- Add resources with `@mcp.resource()` decorator
- Use URI templates for dynamic resources: `"resource://{param}"`
- Add prompts with `@mcp.prompt()` decorator
- Return strings or Message lists from prompts
### Code Quality
- Use type hints for all function parameters and returns
- Write docstrings for tools, resources, and prompts
- Follow PEP 8 style guidelines
- Use async/await for asynchronous operations
- Implement context managers for resource cleanup
- Add inline comments for complex logic
## Example Tool Types to Consider
- Data processing and transformation
- File system operations (read, analyze, search)
- External API integrations
- Database queries
- Text analysis or generation (with sampling)
- System information retrieval
- Math or scientific calculations
## Configuration Options
- **For stdio Servers**:
- Simple direct execution
- Test with `uv run mcp dev server.py`
- Install to Claude: `uv run mcp install server.py`
- **For HTTP Servers**:
- Port configuration via environment variables
- Stateless mode for scalability: `stateless_http=True`
- JSON response mode: `json_response=True`
- CORS configuration for browser clients
- Mounting to existing ASGI servers (Starlette/FastAPI)
## Testing Guidance
- Explain how to run the server:
- stdio: `python server.py` or `uv run server.py`
- HTTP: `python server.py` then connect to `http://localhost:PORT/mcp`
- Test with MCP Inspector: `uv run mcp dev server.py`
- Install to Claude Desktop: `uv run mcp install server.py`
- Include example tool invocations
- Add troubleshooting tips
## Additional Features to Consider
- Context usage for logging, progress, and notifications
- LLM sampling for AI-powered tools
- User input elicitation for interactive workflows
- Lifespan management for shared resources (databases, connections)
- Structured output with Pydantic models
- Icons for UI display
- Image handling with Image class
- Completion support for better UX
## Best Practices
- Use type hints everywhere - they're not optional
- Return structured data when possible
- Log to stderr (or use Context logging) to avoid stdout pollution
- Clean up resources properly
- Validate inputs early
- Provide clear error messages
- Test tools independently before LLM integration
Generate a complete, production-ready MCP server with type safety, proper error handling, and comprehensive documentation.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.