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lumishoang

OpenRouter MCP Server

by lumishoang

get_model

Retrieve detailed specifications for any AI model on OpenRouter, including pricing, context limits, and capabilities, to inform model selection decisions.

Instructions

Get detailed info for one model.

Args: model_id: Model slug, e.g. 'anthropic/claude-sonnet-4.6'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Get detailed info' suggests a read-only operation, it doesn't specify whether this requires authentication, has rate limits, what kind of details are returned (e.g., metadata, capabilities), or potential errors (e.g., invalid model_id). The description is minimal and lacks behavioral context beyond the basic action.

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 appropriately sized and front-loaded: the first sentence states the core purpose clearly, and the 'Args' section efficiently documents the parameter with an example. There's no wasted text, and the structure separates general description from parameter details effectively.

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

Completeness4/5

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

Given the tool's low complexity (single parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the purpose and parameter semantics adequately. However, without annotations, it could benefit from more behavioral context (e.g., read-only nature, error handling) to fully compensate for the lack of structured metadata.

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

Parameters4/5

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

The schema description coverage is 0%, so the description must compensate. It adds meaningful semantics by explaining that 'model_id' is a 'Model slug' and provides a concrete example ('anthropic/claude-sonnet-4.6'), which clarifies the expected format beyond what the schema's generic string type indicates. This is valuable context for the single parameter.

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's purpose with a specific verb ('Get') and resource ('detailed info for one model'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_models' (which presumably lists multiple models) or 'compare_models' (which compares models), though the singular 'one model' phrasing provides some implicit distinction.

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

Usage Guidelines3/5

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

The description implies usage context through 'for one model' and the example model_id format, suggesting this is for retrieving details of a specific known model. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'list_models' (for browsing) or 'search_models' (for finding models), nor does it mention prerequisites or exclusions.

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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