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Openrouter

by io.github.pipeworx-io
OverviewDiscussionsAdmin

List Models

list_models
Read-onlyIdempotent

List LLMs from the OpenRouter catalog (~340 models across all major providers) with normalized $/1M-token pricing, context windows, and modality. Filter by search, free-only, minimum context, or modality (e.g. "image" for vision models). Sorted cheapest-first by completion price. Keyless.

Input Schema

NameRequiredDescriptionDefault
limitNoMax models to return (default 25, max 60).
searchNoCase-insensitive substring match against model id, name, and description. e.g. "claude", "gpt-4o", "deepseek".
modalityNoSubstring match on architecture.modality, e.g. "image" to find vision/multimodal models, "text->text" for text-only.
free_onlyNoIf true, only return models whose prompt price is $0 (free tier).
min_contextNoMinimum context_length in tokens, e.g. 100000 or 200000.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 10,
+    "search": "claude"
+  },
+  {
+    "free_only": true,
+    "min_context": 100000,
+    "modality": "image"
+  }
+]
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds that results are sorted cheapest-first and require no keys, but does not disclose pagination or rate limits. Value is added but limited given annotation coverage.

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 two sentences, front-loading the main purpose. It is concise and includes key details without extraneous text. Slight improvement could be breaking into shorter sentences.

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?

Despite lacking an output schema, the description summarizes what is returned (pricing, context windows, modality). Parameters are well-covered. The tool is a simple listing, so completeness is adequate.

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?

Schema coverage is 100% with each parameter described. The description adds value by providing examples and clarifying default/max for limit, and usage hints like 'free_only' meaning $0 prompt price. This adds meaning beyond 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 clearly states the tool lists LLMs from OpenRouter catalog with specific attributes like pricing, context windows, and modality. It is a specific verb+resource and distinguishes itself from broader tool context.

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 provides filtering options and notes it is keyless, but does not explicitly state when to use this tool versus alternatives like get_model or compare_models. Usage context is inferred but not clearly delimited.

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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