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Filter Models

filter_models
Read-onlyIdempotent

Search OpenRouter models by cost, context length, and vision support. Apply filters to pinpoint models fitting your requirements for price, token capacity, and image inputs.

Instructions

Filter and search available OpenRouter models based on requirements (e.g. cost, context window, vision)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of models to return (default: 10, max: 50)
queryNoFuzzy search term matching model ID or name (e.g., 'claude', 'gemini')
supports_visionNoFilter for models supporting image/vision inputs
min_context_lengthNoMinimum context length in tokens
max_price_per_1m_promptNoMaximum prompt price in USD per 1,000,000 tokens
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, indicating a safe, non-mutating operation. The description adds that it searches 'available' models, implying a live view, but does not disclose other behavioral traits such as return format, pagination, or any API-specific quirks. Since annotations cover the safety profile, a 3 is appropriate.

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, front-loaded sentence: 'Filter and search available OpenRouter models based on requirements (e.g. cost, context window, vision)'. Every word contributes to conveying purpose, and the examples make it concrete without verbosity.

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?

For a read-only filter tool with 5 optional parameters and no output schema, the description is sufficient to understand core behavior, especially since annotations confirm safety and the schema fully documents parameters. However, the return format (e.g., which fields are included) is not mentioned, which could be useful. It is nearly complete for a straightforward filtered-list tool.

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 description coverage is 100%, with each parameter well-described (e.g., 'limit' has default and max, 'query' includes example). The description adds examples like cost, context window, and vision, which map to parameters, but the schema already provides exhaustive meaning. Baseline 3 is justified because the description neither adds nor detracts from the schema.

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 'Filter and search available OpenRouter models based on requirements (e.g. cost, context window, vision)' clearly states a specific verb ('filter and search') and resource ('OpenRouter models') with a concrete scope (requirements like cost, context, vision). This distinguishes it from sibling tools such as list_models (which likely returns all models) and recommend_model (which suggests a single model).

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

Usage Guidelines4/5

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

The description implies usage when the user has specific filtering criteria, but it does not explicitly mention when not to use this tool or directly reference alternatives like list_models. It provides clear context for when to use it (e.g., 'based on requirements') but lacks explicit exclusions or named alternative tools.

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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