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gemini_functionCall

Facilitates function calling with Google Gemini models by accepting function declarations and returning text responses or function call requests based on the prompt. Does not execute functions directly.

Instructions

Generates content using a specified Google Gemini model, enabling the model to request execution of predefined functions. This tool accepts function declarations and returns either the standard text response OR the details of a function call requested by the model. NOTE: This tool only returns the request for a function call; it does not execute the function itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
functionDeclarationsYesRequired. An array of function declarations (schemas) that the model can choose to call based on the prompt.
generationConfigNoOptional configuration for controlling the generation process.
modelNameNoOptional. The name of the Gemini model to use (e.g., 'gemini-1.5-flash'). If omitted, the server's default model (from GOOGLE_GEMINI_MODEL env var) will be used.
promptYesRequired. The text prompt to send to the Gemini model.
safetySettingsNo
toolConfigNoOptional configuration for tools, specifically function calling.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It does disclose key behavioral traits: the tool returns either text responses OR function call requests (not both), and it only returns function call requests without executing them. However, it doesn't mention other important behaviors like rate limits, authentication needs, error handling, or response formats. The description adds some value but leaves significant gaps for a complex tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and well-structured: 4 sentences total, with the first stating the core purpose, the second explaining input/output behavior, and the final two providing crucial behavioral clarification about function call requests. Every sentence earns its place, and key information is front-loaded. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 parameters, nested objects, no output schema, no annotations), the description is incomplete. While it covers the core purpose and key behavioral distinction about function call requests, it doesn't explain the tool's role in a larger workflow, how to handle the returned function call requests, or what the response structure looks like. For a function-calling tool with no output schema, more context about expected outputs would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 83% (high), so the baseline is 3. The description doesn't add any parameter-specific information beyond what's in the schema - it mentions 'function declarations' and 'prompt' generically but provides no additional syntax, format, or usage details. The schema already thoroughly documents all 6 parameters with good descriptions, so the description adds no meaningful parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generates content using a specified Google Gemini model, enabling the model to request execution of predefined functions.' It specifies the verb ('generates content'), resource ('Google Gemini model'), and key capability ('enabling function call requests'). However, it doesn't explicitly differentiate from sibling tools like 'gemini_generateContent' or 'gemini_sendMessage' beyond mentioning function calling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides some implied usage context by stating 'This tool only returns the *request* for a function call; it does not execute the function itself,' which helps distinguish it from execution tools. However, it doesn't explicitly state when to use this vs. alternatives like 'gemini_generateContent' (no function calling) or 'gemini_sendFunctionResult' (handles function results). No explicit when-not-to-use guidance or named alternatives are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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