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Gemini MCP Server

Gemini MCP Server

by salviz
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JavaScript
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gemini_count_tokens

Count tokens in text using a Gemini model to estimate API usage and costs.

Instructions

Count the number of tokens in the provided text using a Gemini 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_count_tokens"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to count tokens for
modelNoModel to use (default: gemini-3.1-pro-preview)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It does not mention side effects, read-only nature, permissions, or any other behavioral traits beyond counting tokens.

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?

The description is a single, clear, and concise sentence with no unnecessary words, making it efficiently front-loaded.

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 token counting tool with 2 parameters and no output schema, the description is minimally adequate. However, it lacks usage guidelines and behavioral transparency, leaving some context gaps.

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%, but the description adds minimal extra meaning beyond the schema. It repeats 'text' and implies 'model' but does not elaborate on format or constraints, 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 counts tokens in provided text using a Gemini model. The verb 'Count' and resource 'tokens' are specific, and no sibling tool performs token counting, making it distinguishable.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools, such as chat or analysis tools, explicit usage context is missing.

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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