Lodestar MCP Server

from mcp.server.fastmcp import FastMCP # from mcp.common.messages import ContextRequest, ContextResponse # Corrected import from typing import Dict, Any, List, Optional from pydantic import BaseModel, Field, validator from datetime import datetime from uuid import UUID, uuid4 # class ContextMetadata(BaseModel): # """Metadata for MCP server responses. # THIS IS NOT FINALIZED!! # Attributes: # code_snippets (List[str]): List of relevant code snippets # doc_links (List[str]): List of related documentation links # timestamp (datetime): When the response was generated # additional_info (Dict[str, Any]): Any additional metadata # """ # code_snippets: List[str] = Field(default_factory=list) # doc_links: List[str] = Field(default_factory=list) # timestamp: datetime = Field(default_factory=datetime.utcnow) # additional_info: Dict[str, Any] = Field(default_factory=dict) class ContextResponse(BaseModel): """Response model for MCP server. The response includes the main text content, a unique identifier, and structured metadata about the response. """ text: str = Field(..., description="Main response text or answer") error: str = Field(default="", description="Error message") context_id: UUID = Field(default_factory=uuid4, description="Unique response identifier") # metadata: ContextMetadata = Field(..., description="Structured response metadata") class ContextRequest(BaseModel): """Container for MCP server requests. Attributes: query (str): The query text from the user api_key (str): Authentication key for the request project_id (str): Identifier for the target project """ query: str api_key: str project_id: str # Create an MCP server mcp = FastMCP("DocQueryServer") # --- Placeholder functions for authentication, index loading, RAG, logging, tracing --- def authenticate_api_key(api_key): """Authenticate the provided API key. Args: api_key (str): The API key to validate Returns: bool: True if authentication successful, False otherwise """ return True # return api_key == "valid-api-key" # Example: hardcoded valid key def load_project_index(project_id): """Load the document index for a specific project. Args: project_id (str): Unique identifier for the project Returns: dict: Project index data containing: - index_type (str): Type of the index - project_id (str): Project identifier """ return {"index_type": "placeholder", "project_id": project_id} # Placeholder index def run_rag_query(index, query_text): """Execute a RAG (Retrieval-Augmented Generation) query against the project index. Args: index (dict): The project index to query against query_text (str): The user's query text Returns: dict: Query results containing: - answer_text (str): Generated answer text - code_snippets (list): Relevant code snippets - doc_links (list): Related documentation links """ return { "answer_text": f"RAG answer for: '{query_text}' (placeholder)", "code_snippets": ["#Placeholder code from RAG"], "doc_links": ["https://placeholder-doc-link-from-rag.com"] } # Placeholder RAG result def log_error(error_data): """Log error information for monitoring and debugging. Args: error_data (dict): Error information including: - api_key (str): API key from the request - project_id (str): Project identifier - query_text (str): Original query text - error_type (str): Type of error - error_message (str): Error description Returns: dict: Logging status containing: - status (str): Status of the logging operation - data (dict): The logged error data """ return {"status": "error_logged", "data": error_data} def trace_answer(trace_data): """Record answer tracing information for analytics and improvement. Args: trace_data (dict): Tracing information including: - api_key (str): API key from the request - project_id (str): Project identifier - query_text (str): Original query text - context_response (dict): The generated response Returns: dict: Tracing status containing: - status (str): Status of the tracing operation - data (dict): The traced answer data """ return {"status": "answer_traced", "data": trace_data} # --- End Placeholders --- @mcp.resource("echo://{message}") def echo_resource(message: str) -> str: """Echo a message as a resource""" return f"Resource echo: {message}" @mcp.tool() def doc_query(query: str, api_key: str, project_id: str)-> Dict[str, Any]: """Query project documentation. Args: query (str): The query text from the user api_key (str): Authentication key for the request project_id (str): Identifier for the target project Returns: ContextResponse: The generated response """ try: # 1. Authentication if not authenticate_api_key(api_key): return ContextResponse( text="Authentication failed.", error="authentication_failed", # metadata=ContextMetadata(additional_info={"error": "authentication_failed"}) ).model_dump() # 2. Project Context Loading project_index = load_project_index(project_id) # 3. RAG Query Processing rag_result = run_rag_query(project_index, query) # 4. Create ContextResponse from RAG result response_metadata = { "code_snippets": rag_result.get("code_snippets", []), "doc_links": rag_result.get("doc_links", []), } response = ContextResponse( text=rag_result.get("answer_text", "No answer from RAG.") ) # 5. Answer Tracing (for successful responses) trace_data = { "api_key": api_key, "project_id": project_id, "query_text": query, "context_response": response.model_dump() # Convert response to dictionary for logging } trace_answer(trace_data) return response except Exception as e: # 6. Error Logging (for any exceptions) error_data = { "api_key": api_key, "project_id": project_id, "query_text": query, "error_type": type(e).__name__, "error_message": str(e), # "traceback": ... (include traceback if needed) } log_error(error_data) return ContextResponse( text=str(e), error="processing_failed" # metadata=ContextMetadata(additional_info={ # "error": "processing_failed", # "error_type": type(e).__name__, # "error_message": str(e) # }) ).model_dump() # return { # "text": "Error processing your query.", # "context_id": "error-processing", # "metadata": {} # } if __name__ == "__main__": print("Starting MCP server...") mcp.run()