Polytomic MCP Server

Instructions

Implementation Reference

• src/polytomic_mcp/server.py:189-225 (handler)

  The `update_model` function acts as an MCP tool handler to update an existing data model in Polytomic. It fetches the current model state, merges it with new parameters, and performs a PUT request to the API.

  @mcp.tool()
  async def update_model(
      id: str,
      name: str | None = None,
      configuration: str | None = None,
      identifier: str | None = None,
      tracking_columns: str | None = None,
  ) -> str:
      """Update an existing data model in Polytomic.
  
      Args:
          id: The model ID to update
          name: Optional new name for the model
          configuration: Optional JSON string with model config (e.g. {"query": "SELECT * FROM users"})
          identifier: Optional field name to use as unique identifier
          tracking_columns: Optional JSON array of column names for change tracking
      """
      current = await polytomic_request(f"/models/{id}")
      current_data = current.get("data", current)
  
      body = {
          "name": name or current_data.get("name"),
          "connection_id": current_data.get("connection_id"),
          "configuration": json.loads(configuration) if configuration else current_data.get("configuration"),
      }
      if identifier:
          body["identifier"] = identifier
      elif current_data.get("identifier"):
          body["identifier"] = current_data.get("identifier")
  
      if tracking_columns:
          body["tracking_columns"] = json.loads(tracking_columns)
      elif current_data.get("tracking_columns"):
          body["tracking_columns"] = current_data.get("tracking_columns")
  
      result = await polytomic_request(f"/models/{id}", method="PUT", body=body)
      return json.dumps(result, indent=2)