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elvatis

elvatis-mcp

Official
by elvatis

gemini_run

Send a prompt to Google Gemini via the local gemini CLI. Direct LLM call with no OpenClaw overhead, using cached Google auth.

Instructions

Send a prompt to Google Gemini via the local gemini CLI. Fast, direct LLM call with no OpenClaw overhead. Uses cached Google auth — no API key required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoGemini model to use, e.g. "gemini-2.5-pro" or "gemini-2.5-flash". Omit to use the configured default (GEMINI_MODEL env var).
promptYesPrompt or question to send to the Gemini AI model.
timeout_secondsNoMax seconds to wait for a response.
working_directoryNoWorking directory for the Gemini process. Set this to the project root so Gemini can read local files. Defaults to the user home directory.
Behavior3/5

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

No annotations are provided, so the description must carry the behavioral burden. It discloses authentication and performance traits but omits error handling, rate limits, or what happens on timeout.

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?

Two short, front-loaded sentences that convey purpose and key differentiator without fluff. Every sentence earns its place.

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 4 parameters, no output schema, and no annotations, the description covers auth, speed, and a working directory hint. It lacks return value info and error/ timeout behavior, leaving gaps for a full understanding.

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?

100% of parameters have schema descriptions, so baseline is 3. The description adds value only for 'working_directory' (hint to set to project root), which is helpful but not extensive.

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 states the verb 'Send a prompt' to 'Google Gemini via the local gemini CLI' and distinguishes it from sibling tools like claude_run and local_llm_run by highlighting 'no OpenClaw overhead' and directness.

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?

It explains the authentication method ('Uses cached Google auth — no API key required') and the speed advantage, implying when to use it. However, it does not explicitly state when not to use it or list alternatives.

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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