Gemini MCP Server

Gemini Chat

gemini_chat

Chat with Google Gemini models by sending messages. Customize responses with temperature and system prompts, and ground answers using Google Search for real-time information.

Instructions

Chat with Google Gemini models

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": {
      "message": "<message>"
    },
    "name": "gemini_chat"
  }
}

Input Schema

Name	Required	Description
`message`	Yes	The message to send
`model`	No	Model to use (defaults to latest available)
`temperature`	No	Controls randomness (0.0 to 1.0)
`max_tokens`	No	Maximum tokens in response
`system_prompt`	No	Optional system instruction
`grounding`	No	Enable Google Search grounding for real-time information
`thinking_level`	No	Thinking depth for Gemini 3 models only. "low" minimises latency for simple tasks. "high" (default for Gemini 3) maximises reasoning depth. "medium"/"minimal" available on Gemini 3 Flash only. Ignored for non-Gemini-3 models.

Output Schema

Name	Required	Description	Default
`content`	Yes
`success`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.3.0Jun 22, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description must disclose behaviors, but it only states the action. It does not mention that the tool mutates state (e.g., conversation history), requires authentication, has rate limits, or that some parameters like thinking_level are model-specific. The input schema covers defaults but not behavioral effects.

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?

The description is a single sentence, which is concise and front-loaded. It opens with the key purpose. However, given the tool's complexity (7 parameters, output schema, many siblings), it may be too brief and omits useful context.

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

Completeness2/5

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

Despite the presence of an output schema, the description lacks context about how this tool fits into the broader toolset. It does not clarify when to prefer this over gemini_agent or gemini_prompt_assistant, and it omits details about conversation state, model selection behavior, or response structure.

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 coverage is 100%, so the schema documents all parameters with type, descriptions, and defaults. The description adds no extra meaning beyond what the schema provides, hence a baseline score of 3 is appropriate.

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 'Chat with Google Gemini models' clearly states the verb (chat) and resource (Gemini models). It distinguishes itself from sibling tools like gemini_deep_research or gemini_agent by focusing on conversational interaction, but does not explicitly differentiate from gemini_agent or gemini_prompt_assistant, which may overlap.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like gemini_agent or gemini_deep_research. There is no mention of use cases, preconditions, or exclusions, leaving the agent without context for choosing among similar tools.

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