fetch
Retrieve complete document contents from search results to access stored knowledge in your local Markdown files.
Instructions
Fetch the full contents of a search result document
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- The handler function for the MCP tool named 'fetch'. It retrieves the full content of a document by its ID using the underlying 'read_note' tool, formats it into a JSON structure compatible with ChatGPT/OpenAI expectations, and returns it wrapped in the MCP content array format.
@mcp.tool(description="Fetch the full contents of a search result document") async def fetch( id: str, context: Context | None = None, ) -> List[Dict[str, Any]]: """ChatGPT/OpenAI MCP fetch adapter returning a single text content item. Args: id: Document identifier (permalink, title, or memory URL) context: Optional FastMCP context passed through for auth/session data Returns: List with one dict: `{ "type": "text", "text": "{...JSON...}" }` where the JSON body includes `id`, `title`, `text`, `url`, and metadata. """ track_mcp_tool("fetch") logger.info(f"ChatGPT fetch request: id='{id}'") try: # ChatGPT tools don't expose project parameter, so use default project config = ConfigManager().config default_project = config.default_project # Call underlying read_note function content = await read_note.fn( identifier=id, project=default_project, # Use default project for ChatGPT page=1, page_size=10, # Default pagination context=context, ) # Format the document for ChatGPT document = _format_document_for_chatgpt(content, id) logger.info(f"Fetch completed: id='{id}', content_length={len(document.get('text', ''))}") # Return in MCP content array format as required by OpenAI return [{"type": "text", "text": json.dumps(document, ensure_ascii=False)}] except Exception as e: logger.error(f"ChatGPT fetch failed for id '{id}': {e}") error_document = { "id": id, "title": "Fetch Error", "text": f"Failed to fetch document: {str(e)[:200]}", "url": id, "metadata": {"error": "Fetch failed"}, } return [{"type": "text", "text": json.dumps(error_document, ensure_ascii=False)}]