Gemini MCP Server

by salviz

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JavaScript

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gemini_embed

Generate text embeddings for semantic similarity, retrieval, and classification tasks using a Gemini embedding model.

Instructions

Generate text embeddings using a Gemini embedding model

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": {
      "text": "<text>"
    },
    "name": "gemini_embed"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The text to generate embeddings for
`model`	No	Embedding model to use (default: gemini-embedding-001)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Jun 29, 2026

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, and the description lacks behavioral traits such as auth needs, rate limits, or side effects. It only states the core function, leaving the agent without transparency on what happens beyond generation.

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, concise and to the point. However, it could include more context without becoming wordy, so slightly above average.

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?

With 2 parameters and no output schema, the description is complete enough for a simple embedding tool but lacks details on output format or embedding dimensions, leaving some gaps for the agent.

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%, and both parameters are described in the schema. The description adds no extra meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 tool generates text embeddings using a Gemini embedding model, which is a specific verb+resource. It distinguishes from sibling tools that focus on analysis, chat, or other tasks.

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, such as when to choose a different model or when not to use embeddings. No context on prerequisites or limitations.

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