how to ground an llm answer to real tools?

Question

Accepted Answer

You need a structured integration that maps the model’s output to executable functions and feeds results back into the context. This requires three components.

1.  **Define Tool Schemas**
    Provide a JSON schema in your API request. It defines the function name, description, and input parameters. Use `type: "object"` with `properties` and `required` fields. Frameworks like Vercel AI SDK or LangChain automate this conversion from Zod or Pydantic models.

```json
    {
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "City name"}
          },
          "required": ["location"]
        }
      }
    }
    ```

2.  **Enable Tool Integration**
    Use an LLM tool integration framework such as AG2, Unified Agent SDK, or native MCP to connect the model to external data.

*   **Standard Function Calling:** Pass the schema in the `tools` parameter. When the model triggers a call, your application executes the function and returns the result.
    *   **MCP Integration:** Use the Model Context Protocol for standardized connections to local or remote servers.

```python
    tools=[{
        "type": "mcp",
        "server_label": "web_search_preview",
        "server_url": "http://localhost:8888",
        "allowed_tools": ["search"]
    }]
    ```

3.  **Integrate Results Back into Context**
    After your application executes the tool, send the result back to the model to complete the multi-step flow.

*   **Anthropic:** Parse `tool_use` blocks from the model, execute the function, and return the output as `tool_result` content.
    *   **OpenAI/Gemini:** Send the execution result back to the model in the subsequent API call so it can generate a final response based on the real-time data.

**Pending Action:**
To proceed with implementing this for **Liminality**, I need to know which specific external tools or APIs you intend to ground the LLM to. Provide the list of target tools or APIs.