Skip to main content
Glama

FastMCP Boilerplate

by rainer85ah
chat.py3.25 kB
from pydantic import Field from agents.code import code_mcp from fastmcp.prompts.prompt import Message def build_prompt(system: str, user: str) -> list[Message]: return [ Message(role="system", content=system), Message(role="user", content=user) ] @code_mcp.prompt( "generate_code", description="Prompt to generate idiomatic source code from a natural prompt." ) def prompt_generate_code( task_description: str = Field(description="The natural language description of code to generate"), language: str = Field(default="python", description="Target programming language"), ) -> list[Message]: instruction_map = { "python": "Respond with well-structured Python code including functions, docstrings, and comments.", "javascript": "Respond with idiomatic JavaScript using modern ES6+ syntax and inline comments.", "typescript": "Respond with TypeScript using proper type annotations and best practices.", "bash": "Write a Bash shell script with comments explaining each major step.", "go": "Generate Go code with idiomatic structure and comments." } system_message = instruction_map.get( language.lower(), f"Write {language} code using best practices and clear structure." ) return build_prompt(system_message, task_description) @code_mcp.prompt( "fix_code_tool", description="Prompt to refactor and correct code issues." ) def prompt_fix_code( code: str = Field(description="Code snippet to fix or refactor.") ) -> list[Message]: return build_prompt( "You are a senior engineer skilled in debugging and refactoring.", f"Fix and improve the following code:\n\n{code}" ) @code_mcp.prompt( "explain_code_tool", description="Prompt to explain what a code snippet does." ) def prompt_explain_code( code: str = Field(description="Code snippet to explain.") ) -> list[Message]: return build_prompt( "You are a teacher explaining code to beginners.", f"Explain this code clearly:\n\n{code}" ) @code_mcp.prompt( "write_tests_tool", description="Prompt to generate test cases for the given code." ) def prompt_write_tests( code: str = Field(description="Code to generate unit tests for."), language: str = Field(description="Target programming language.") ) -> list[Message]: return build_prompt( f"Write unit tests for the following {language} code.", code ) @code_mcp.prompt( "debug_code_tool", description="Prompt to find bugs and logical errors in code." ) def prompt_debug_code( code: str = Field(description="Code to debug.") ) -> list[Message]: return build_prompt( "You are an expert debugger. Identify any bugs or issues in this code.", code ) @code_mcp.prompt( "generate_function_docstring_tool", description="Prompt to write a docstring for a function." ) def prompt_docstring( code: str = Field(description="Function code to document.") ) -> list[Message]: return build_prompt( "You are a documentation expert. Generate a clean and informative docstring.", f"Write a docstring for this function:\n\n{code}" )

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/rainer85ah/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server