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OpenRouter MCP Multimodal Server

OpenRouter MCP Multimodal Server

by stabgan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

get_model_info

Read-onlyIdempotent

Get detailed information about a specific AI model to understand its capabilities and choose the right one for your multimodal tasks.

Instructions

Get details about a specific 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": {
      "model": "<model>"
    },
    "name": "get_model_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.8.2

TDQS

C2.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, which cover the behavioral profile. The description adds no additional behavioral context beyond stating it retrieves details, so it meets the baseline but does not exceed annotations.

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 concise sentence with no redundant words. However, it is slightly too brief and could benefit from additional context without becoming verbose.

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?

Given no output schema and a single parameter, the description should at least hint at what kind of details are returned (e.g., capabilities, metadata). The current description is too minimal to fully inform an agent about the tool's output or usage prerequisites.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has one parameter 'model' with no description, and the tool description does not clarify what the parameter expects (e.g., model name, ID, or exact string). With 0% schema description coverage, the description should compensate but fails to add meaning.

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 clearly states the tool gets details about a specific model, which distinguishes it from siblings like search_models (searching for models) and other generation tools. However, it does not specify what constitutes 'details', leaving some ambiguity.

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 like search_models, nor does it mention when to avoid using it. No context or exclusions are given.

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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