OpenRouter MCP Server

compare_models

Compare responses from multiple AI models by sending a single message. Evaluate outputs to choose the best model for your task.

Instructions

Compare responses from multiple 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": {
      "models": "<models>",
      "message": "<message>"
    },
    "name": "compare_models"
  }
}

Input Schema

Name	Required	Description
`models`	Yes	Array of model IDs to compare
`message`	Yes	Message to send to all models
`max_tokens`	No	Maximum tokens per response

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 27, 2026

TDQS

C2.6/5.0

Behavior1/5

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

No annotations provided; description only says 'compare responses' without disclosing behavioral traits such as whether it is read-only, how errors are handled, or the format of the output (e.g., does it return all responses or a summary?). This is a critical gap.

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

Conciseness3/5

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

The description is a single short sentence, which is concise but lacks necessary detail. It is not verbose, but the under-specification reduces informativeness, balancing to a mediocre score.

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?

For a tool that compares models, agents need return format, ordering, and error handling information. The description provides none of this, and with no output schema or annotations, the completeness is poor.

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?

Input schema coverage is 100% with descriptions for all three parameters (models, message, max_tokens). The tool description adds no additional 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.

Purpose4/5

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

The description clearly states it compares responses from multiple models, distinguishing it from sibling tools like chat_with_model (single model) and list_models (model listing). However, it lacks specificity about the comparison mechanism (e.g., side-by-side display).

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 chat_with_model. Does not mention scenarios where comparing responses is beneficial or when a different tool would be more appropriate.

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

Deploy Server