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GlitterKill

Gemini MCP Ultimate

by GlitterKill

ask-gemini

Analyze large codebases or answer questions by sending prompts to Google Gemini, with model selection, sandboxed execution, session resumption, and structured edit suggestions.

Instructions

model selection [-m], sandbox [-s], and changeMode:boolean for providing edits

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoOptional model to use (e.g., 'gemini-3-flash-preview'). If not specified, uses the default model (gemini-3-pro-preview).
promptYesAnalysis request. Use @ syntax to include files (e.g., '@largefile.js explain what this does') or ask general questions
sandboxNoUse sandbox mode (-s flag) to safely test code changes, execute scripts, or run potentially risky operations in an isolated environment
changeModeNoEnable structured change mode - formats prompts to prevent tool errors and returns structured edit suggestions that Claude can apply directly
chunkIndexNoWhich chunk to return (1-based)
session_idNoSession ID or index to resume a previous conversation context (maps to --resume)
allowed_toolsNoSpecific tools to allow without confirmation (overrides approval_mode)
approval_modeNoAutonomy control: 'default' (ask), 'auto_edit' (allow edits), 'yolo' (allow all)yolo
chunkCacheKeyNoOptional cache key for continuation
output_formatNoOutput format: 'text' (default), 'json', or 'stream-json'
experimental_acpNoEnable experimental ACP (Agentic Coding Protocol) mode
include_directoriesNoAdditional directories to include in the workspace context

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.3

TDQS

D1.9/5.0
Behavior2/5

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

With no annotations, the description carries the full behavioral burden, and it discloses almost nothing: not that this makes an external LLM call, nor latency, cost, permissions, or what the response looks like. The only hints ('sandbox', 'for providing edits') restate schema semantics rather than adding behavioral context. It does not contradict anything, but it leaves the agent uninformed.

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

Conciseness2/5

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

It is short, but shortness here is under-specification, not conciseness: it is a sentence fragment with no purpose statement to front-load. The listed modifiers (model, sandbox) are not the most important information an agent needs to invoke the tool.

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

Completeness1/5

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

A 12-parameter tool with no annotations and no output schema requires a substantive description, and this provides essentially none. Nothing about model defaults, session resumption, approval autonomy, or the external-call nature is explained at the description level.

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% and the schema itself documents all 12 parameters in detail, so the baseline is 3 even with no real param explanation in the description. The description adds only trivial flag aliases ('[-m]', '[-s]') and the type of changeMode, nothing beyond the schema.

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

Purpose2/5

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

The text never states what the tool actually does — it never says it sends a prompt to Gemini or returns analysis. It only enumerates a few parameters (model, sandbox, changeMode), which is a parameter summary rather than a purpose statement. An agent cannot tell from this how ask-gemini differs from siblings like brainstorm or manage-sessions.

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

Usage Guidelines1/5

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

There is no when-to-use guidance, no prerequisites, and no mention of alternatives despite seven sibling tools. The fragments 'sandbox [-s]' and 'changeMode:boolean for providing edits' are labels, not conditions for choosing this tool.

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