플러그인 및 API 연동용 MCP 서버 구축하기

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211# Replace the illustrative IDs and URLs below with your own resource values.
"""
Sample MCP Server for ChatGPT Integration

This server implements the Model Context Protocol (MCP) with search and fetch
capabilities designed to work with ChatGPT's chat and deep research features.
"""

import logging
import os
from typing import Any

from fastmcp import FastMCP
from openai import OpenAI
from pydantic import BaseModel


class SearchResult(BaseModel):
    id: str
    title: str
    url: str


class SearchOutput(BaseModel):
    results: list[SearchResult]


class FetchOutput(BaseModel):
    id: str
    title: str
    text: str
    url: str
    metadata: dict[str, Any] | None = None


# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# OpenAI configuration
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
VECTOR_STORE_ID = "vs_123"

# Initialize OpenAI client
openai_client = OpenAI(api_key=OPENAI_API_KEY)

server_instructions = """
This MCP server provides search and document retrieval capabilities
for ChatGPT Apps and deep research. Use the search tool to find relevant documents
based on keywords, then use the fetch tool to retrieve complete
document content with citations.
"""


def create_server():
    """Create and configure the MCP server with search and fetch tools."""

    # Initialize the FastMCP server
    mcp = FastMCP(name="Sample MCP Server", instructions=server_instructions)

    @mcp.tool(output_schema=SearchOutput.model_json_schema())
    async def search(query: str) -> SearchOutput:
        """
        Search for documents using OpenAI Vector Store search.

        This tool searches through the vector store to find semantically relevant matches.
        Returns a list of search results with basic information. Use the fetch tool to get
        complete document content.

        Args:
            query: Search query string. Natural language queries work best for semantic search.

        Returns:
            Dictionary with 'results' key containing list of matching documents.
            Each result includes id, title, and URL.
        """
        if not query or not query.strip():
            return SearchOutput(results=[])

        if not openai_client:
            logger.error("OpenAI client not initialized - API key missing")
            raise ValueError("OpenAI API key is required for vector store search")

        # Search the vector store using OpenAI API
        logger.info(f"Searching {VECTOR_STORE_ID} for query: '{query}'")

        response = openai_client.vector_stores.search(
            vector_store_id=VECTOR_STORE_ID, query=query
        )

        results = []

        # Process the vector store search results
        if hasattr(response, "data") and response.data:
            for i, item in enumerate(response.data):
                # Extract file_id, filename, and content
                item_id = getattr(item, "file_id", f"vs_{i}")
                item_filename = getattr(item, "filename", f"Document {i + 1}")

                result = SearchResult(
                    id=item_id,
                    title=item_filename,
                    url=f"https://platform.openai.com/storage/files/{item_id}",
                )

                results.append(result)

        logger.info(f"Vector store search returned {len(results)} results")
        return SearchOutput(results=results)

    @mcp.tool(output_schema=FetchOutput.model_json_schema())
    async def fetch(id: str) -> FetchOutput:
        """
        Retrieve complete document content by ID for detailed
        analysis and citation. This tool fetches the full document
        content from OpenAI Vector Store. Use this after finding
        relevant documents with the search tool to get complete
        information for analysis and proper citation.

        Args:
            id: File ID from vector store (file-xxx) or local document ID

        Returns:
            Complete document with id, title, full text content,
            optional URL, and metadata

        Raises:
            ValueError: If the specified ID is not found
        """
        if not id:
            raise ValueError("Document ID is required")

        if not openai_client:
            logger.error("OpenAI client not initialized - API key missing")
            raise ValueError(
                "OpenAI API key is required for vector store file retrieval"
            )

        logger.info(f"Fetching content from vector store for file ID: {id}")

        # Fetch file content from vector store
        content_response = openai_client.vector_stores.files.content(
            vector_store_id=VECTOR_STORE_ID, file_id=id
        )

        # Get file metadata
        file_info = openai_client.vector_stores.files.retrieve(
            vector_store_id=VECTOR_STORE_ID, file_id=id
        )

        # Extract content from paginated response
        file_content = ""
        if hasattr(content_response, "data") and content_response.data:
            # Combine all content chunks from FileContentResponse objects
            content_parts = []
            for content_item in content_response.data:
                if hasattr(content_item, "text"):
                    content_parts.append(content_item.text)
            file_content = "\n".join(content_parts)
        else:
            file_content = "No content available"

        # Use filename as title and create proper URL for citations
        filename = openai_client.files.retrieve(file_id=id).filename

        result = FetchOutput(
            id=id,
            title=filename,
            text=file_content,
            url=f"https://platform.openai.com/storage/files/{id}",
        )

        # Add metadata if available from file info
        if hasattr(file_info, "attributes") and file_info.attributes:
            result.metadata = dict(file_info.attributes)

        logger.info(f"Fetched vector store file: {id}")
        return result

    return mcp


def main():
    """Main function to start the MCP server."""
    logger.info(f"Using vector store: {VECTOR_STORE_ID}")

    # Create the MCP server
    server = create_server()

    # Configure and start the server
    logger.info("Starting MCP server on 0.0.0.0:8000")
    logger.info("Server will be accessible via SSE transport")

    try:
        # Use FastMCP's built-in run method with SSE transport
        port = int(os.environ.get("OPENAI_EXAMPLE_PORT", "8000"))
        server.run(
            transport="sse",
            host="0.0.0.0",
            port=port,
            uvicorn_config={"loop": "asyncio"},
        )
    except KeyboardInterrupt:
        logger.info("Server stopped by user")
    except Exception as e:
        logger.error(f"Server error: {e}")
        raise


if __name__ == "__main__":
    main()