List available AI models on OpenRouter with filters for name, slug, context window size, and pricing.
List available AI models on OpenRouter with filters for name, slug, context window size, and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of models to return (default: 30, max: 100). | |
| search | No | Search keyword for model name or slug (e.g. 'claude', 'gemini', 'deepseek', 'free'). |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does not disclose pagination behavior, rate limits, authentication needs, or what the response looks like; 'available' is the only hint about scope. For a discovery tool with zero annotation coverage this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no padding — the verb and resource lead. The only flaw is the inaccurate filter tail, not the length or ordering.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should say something about returned fields, sorting, or pagination limits, and it does not. Worse, the claimed filters (context window, pricing) are absent from the schema, so the description is incomplete and partly inaccurate for a two-parameter discovery tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the real parameters (limit, search) are documented in the schema. However, the description advertises filters for 'name, slug, context window size, and pricing' that do not exist as parameters, actively misleading the agent about the tool's inputs rather than adding accurate value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (List) and resource (AI models on OpenRouter), which is clearly distinguishable from siblings like chat_completion and manage_keys. It is clear what the tool does, though the trailing filter list overstates the tool's actual capability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The list-models framing implies the discovery use case before calling chat_completion, but the description never states when to use this versus alternatives or any prerequisite context. Usage is left to inference rather than made explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.