Sibyl

generate

Produce text with Google's Gemini API for prose, code, chat, or reasoning. Supply a prompt and optionally set JSON output, system instructions, or model.

Instructions

Generate text with Gemini: prose, code, chat, reasoning. The core generative call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate"
  }
}

Input Schema

Name	Required	Description
`json`	No	Force a JSON response.
`model`	No	Override the Gemini text model.
`prompt`	Yes	The prompt.
`system`	No	Optional system instruction.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 7, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It only states what the tool does (generate text) and does not disclose return format, non-determinism, authentication requirements, rate limits, or the effect of the json parameter. This is a significant gap for a generative API call.

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 a single, front-loaded sentence that wastes no words. It states the action, the domain, and the tool's role in two concise clauses.

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?

The description is adequate for basic invocation (prompt is required and clear), but with no annotations and no output schema, it leaves unstated details about response format and the behavior of optional parameters like json and system. It is minimally viable but not exhaustive.

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?

The input schema covers all four parameters with descriptions, achieving 100% schema description coverage. The tool description adds no extra parameter-level detail beyond the schema, so the baseline of 3 applies per the rubric.

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

Purpose5/5

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

The description clearly identifies the action ('Generate text with Gemini') and the resource/scope ('prose, code, chat, reasoning'). The phrase 'core generative call' distinguishes this from sibling tools like generate_image and generate_video, making the purpose unambiguous.

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

Usage Guidelines4/5

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

The description provides clear context by enumerating text generation use cases (prose, code, chat, reasoning) and calls it the 'core generative call,' implying this is the primary text tool. It does not explicitly list excluded alternatives, but the sibling tool names (generate_image, generate_video, speak) make the boundary obvious enough.

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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