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elvatis

elvatis-mcp

Official
by elvatis

gemini_run

Send a prompt to Google Gemini via the local CLI. Uses cached Google authentication, so no API key is needed.

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?

With no annotations, the description carries the full behavioral burden. It usefully discloses the execution mechanism ('local gemini CLI'), authentication behavior ('cached Google auth'), and the lack of an API key requirement. But it does not mention failure modes, the dependency on the CLI being installed, potential side effects from working_directory, or what the response contains.

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

Conciseness4/5

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

Three short sentences with the core action front-loaded and each additional sentence conveying a distinct non-obvious fact (speed/no overhead, cached auth/no API key). It is slightly redundant because 'Fast, direct LLM call with no OpenClaw overhead' partially repeats the implications of 'via the local gemini CLI,' but overall it is tight and efficient.

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?

For a simple 4-parameter tool with full schema coverage, the description covers provider, execution method, and auth context. However, because there is no output schema, the description should have mentioned what the tool returns or how errors surface, and it remains silent on exit/error behavior. This is adequate but not complete.

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 100%, so the input schema already documents all four parameters with sufficient detail. The description adds no parameter-specific meaning beyond restating that a prompt is sent, so the baseline score of 3 applies.

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 opens with a specific verb and resource: 'Send a prompt to Google Gemini via the local gemini CLI,' which clearly differentiates it from sibling LLM tools like codex_run, claude_run, and local_llm_run. The extra traits, 'no OpenClaw overhead' and 'no API key required,' further distinguish this as a lightweight, direct Gemini call.

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 implies usage context with 'Fast, direct LLM call with no OpenClaw overhead' and 'Uses cached Google auth - no API key required,' suggesting it is for minimal-overhead Gemini calls or when API keys are not available. However, it does not explicitly state when to use this tool instead of siblings like local_llm_run or codex_run, nor does it mention any exclusions.

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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