extract-mcp-servers-from-content
Extract MCP servers from content by analyzing text to identify and collect server information for integration with the mcp-server-collector system.
Instructions
Extract MCP Servers from given content
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | content containing mcp servers |
Implementation Reference
- src/mcp_server_collector/extract.py:8-30 (handler)Core handler function that uses OpenAI to extract MCP servers from the provided content using a JSON-structured prompt.async def extract_mcp_servers_from_content(content: str) -> str | None: client = OpenAI( api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("OPENAI_BASE_URL"), ) user_content = extract_mcp_servers_prompt.format(content=content) logger.info(f"Extract prompt: {user_content}") chat_completion = client.chat.completions.create( messages=[ { "role": "user", "content": user_content, } ], model=os.getenv("OPENAI_MODEL"), response_format={"type": "json_object"}, ) return chat_completion.choices[0].message.content
- src/mcp_server_collector/server.py:32-45 (registration)Registers the tool in the MCP server's list_tools() method, defining name, description, and input schema.types.Tool( name="extract-mcp-servers-from-content", description="Extract MCP Servers from given content", inputSchema={ "type": "object", "properties": { "content": { "type": "string", "description": "content containing mcp servers", }, }, "required": ["content"], }, ),
- Input schema defining the expected 'content' parameter for the tool.inputSchema={ "type": "object", "properties": { "content": { "type": "string", "description": "content containing mcp servers", }, }, "required": ["content"], },
- MCP server call_tool handler that dispatches to extract logic for this tool and formats the response.@server.call_tool() async def handle_call_tool( name: str, arguments: dict | None ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: if not arguments: raise ValueError("Missing arguments") content = None match name: case "extract-mcp-servers-from-url": url = arguments.get("url") if not url: raise ValueError("Missing url") content = await call_fetch_tool(url) case "extract-mcp-servers-from-content": content = arguments.get("content") case "submit-mcp-server": url = arguments.get("url") avatar_url = arguments.get("avatar_url") or "" result = await submit_mcp_server(url, avatar_url) content = json.dumps(result) return [ types.TextContent( type="text", text=content, ) ] case _: raise ValueError(f"Unknown tool: {name}") if not content: raise ValueError("Missing content") logger.info(f"Fetched content from {url}: {content}") mcp_servers = await extract_mcp_servers_from_content(content) if not mcp_servers: raise ValueError("Extracted no MCP Servers") logger.info(f"Extracted MCP Servers from {url}: {mcp_servers}") return [ types.TextContent( type="text", text=mcp_servers, ) ]
- Prompt template used in the extraction handler to guide the LLM in parsing MCP server details from content.extract_mcp_servers_prompt = """Please extract all MCP Servers from the following content and return a JSON array. Each item should contain: - name: extracted from the repository name in the URL - title: a human readable title - description: a brief description of the server - url: the full GitHub repository URL - author_name: extracted from the GitHub username in the URL Example response format: [ {{ "name": "mcp-server-example", "title": "MCP Server Example", "description": "A sample MCP server implementation", "url": "https://github.com/username/mcp-server-example", "author_name": "username" }} ] Content to analyze: {content} """