TestMCPToolSet.httprr

Overview

TestMCPToolSet.httprr is an HTTP request/response trace file capturing interactions with the Google Generative Language API endpoint https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent. This file contains two sequential HTTP POST requests and their corresponding JSON responses. The trace illustrates the usage of a Model Context Protocol (MCP) toolset in the context of a language model generating content based on a user query about the weather in a city (specifically "london").

The file demonstrates how a generative language model interacts with function tools, specifically a get_weather function, declared via JSON schema, invoked by the model, and how the results are returned in the response. It effectively serves as a functional test or example of the integration between language models, function tools, and tool invocation workflows.

Detailed Explanation

HTTP Requests

• Endpoint:
  POST https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent

• Purpose:
  To generate content or responses from the Gemini 2.5 Flash model based on input prompts, system instructions, and registered tools.

• Headers:

  • Content-Type: application/json

  • User-Agent: Go-http-client/1.1

  • Content-Length: Varies per request

Payload Structure

Each request JSON payload contains the following key components:

1. contents:
   An array describing the conversation or prompt parts. Each part includes:

   • text: The user input or model-generated text.

   • role: Role of the message sender (e.g., user, model).

2. systemInstruction:
   Instructions given to the model, typically setting the context or capabilities, such as:
   "I can answer your questions about the time and weather in a city."

3. tools:
   An array specifying tools (functions) available to the model, each with:

   • functionDeclarations: Defines the function signature, description, input parameter schema, and response schema in JSON Schema format.

Function Tool: get_weather

• Description:
  Returns a weather summary for a given city.

• Parameters Schema:

  • city (string, required): The city name to query weather for.

• Response Schema:

  • weather_summary (string, required): A description of the weather in the specified city.

Flow of Interactions

First Request/Response Pair

• Request:
  User asks: "what is the weather in london?"
  The system instruction is provided, and the get_weather tool is declared.

• Response:
  The model generates a function call to get_weather with argument: { "city": "london" }. This indicates the model decided to invoke the weather tool rather than respond directly.

Second Request/Response Pair

• Request:
  The user content now includes the original question plus the model's function call to get_weather with {"city": "london"}. The system instruction and tool declaration remain.

• Response:
  The model returns the weather summary: "Today in "london" is sunny" as plain text, indicating successful function execution and content generation.

Important Implementation Details

• Function Calling Mechanism:
  The model supports a function calling interface where it can emit structured calls to tools declared in the request. This mechanism uses JSON schemas for input validation and output formatting, enabling robust integration of external knowledge or computation tools into the language model’s responses.

• Thought Signature:
  The model output includes a thoughtSignature, a cryptographic or unique token presumably for tracing or verifying the reasoning steps the model took to produce the function call.

• Token Usage Metadata:
  Each response includes token usage statistics (promptTokenCount, candidatesTokenCount, totalTokenCount), which are useful for monitoring resource consumption and optimizing prompt design.

Interaction With Other System Components

• This file exemplifies the integration of MCP Toolset Integration (80577) by showing how tools (functions) are declared, invoked, and responded to within LLM prompts and completions.

• The trace also relates to LLM Integration and Agents (80562), illustrating an agent-like flow where the model acts as an orchestrator, deciding to call tools and processing their responses.

• The function tool declaration and invocation mechanism is a concrete example of the Function Tools topic (80570), demonstrating how Go functions or external APIs can be wrapped and exposed to LLMs with automatic JSON schema enforcement.

Example Usage

Step 1: User Query

{
  "contents":[{"parts":[{"text":"what is the weather in london?"}],"role":"user"}],
  "systemInstruction":{"parts":[{"text":"I can answer your questions about the time and weather in a city."}],"role":"user"},
  "tools":[{"functionDeclarations":[{"description":"returns weather in the given city","name":"get_weather","parametersJsonSchema":{"additionalProperties":false,"properties":{"city":{"description":"city name","type":"string"}},"required":["city"],"type":"object"},"responseJsonSchema":{"additionalProperties":false,"properties":{"weather_summary":{"description":"weather summary in the given city","type":"string"}},"required":["weather_summary"],"type":"object"}}]}]}
}

Step 2: Model Calls get_weather

{
  "functionCall": {
    "name": "get_weather",
    "args": {"city": "london"}
  }
}

Step 3: Model Returns Weather Summary

{
  "text": "Today in \"london\" is sunny"
}

Mermaid Diagram

flowchart TD
UserQuery["User Query: what is the weather in london?"]
SystemInstruction["System Instruction: I can answer questions about time and weather."]
ToolDeclaration["Tool Declared: get_weather(city)"]
ModelResponse1["Model Response: functionCall get_weather(city=london)"]
NextRequest["Next Request includes functionCall"]
ModelResponse2["Model Response: text 'Today in \"london\" is sunny'"]
UserQuery -->|sent to| ModelResponse1
SystemInstruction --> ModelResponse1
ToolDeclaration --> ModelResponse1
ModelResponse1 --> NextRequest
NextRequest --> ModelResponse2

Summary of Key Concepts Represented in this File

• Function Tool Declaration: Defining function signatures, descriptions, and JSON schemas for input/output validation.

• Model Function Invocation: The model deciding to call a declared tool based on user input and system instructions.

• Function Response Handling: Injecting the tool's response back into the conversation for final output generation.

• Token Usage Tracking: Monitoring prompt and response tokens for usage and cost management.

• Thought Signature: Metadata for model reasoning traceability.

The interactions captured in this file provide a clear example of how an LLM agent can be augmented with external tools using a well-defined protocol, aligning with the principles described in MCP Toolset Integration and Function Tools.