OpenRouter model catalog — id, name, context_length, pricing, created. Cached 1h, auto-fetched via public API. For model routing decisions.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max models to return, default 100 | |
| min_context | No | Min context length filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With empty annotations, the description carries full behavioral disclosure burden. It adds caching behavior ('Cached 1h') and data source ('auto-fetched via public API'), which are valuable beyond bare minimum. No destructive actions are indicated, and no contradictions exist.
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?
The description is three concise sentences: first lists contents, second covers caching and source, third states purpose. No redundant words, front-loaded with key information. Every sentence earns its place.
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?
For a simple listing tool with two optional parameters and no output schema, the description covers caching, data source, and purpose. It does not detail output format or pagination, but given the low complexity and high schema coverage, it is nearly complete. A 4 is reasonable.
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 description coverage is 100%, so the schema already documents the two parameters (limit, min_context). The description adds overall context but does not enhance parameter meaning beyond what the schema provides. Baseline 3 is appropriate.
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?
The description clearly states the tool lists models from OpenRouter with specific fields (id, name, context_length, pricing, created). It distinguishes itself from diverse sibling tools by focusing on the model catalog, making its purpose unambiguous.
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 description mentions 'For model routing decisions' which implies context but does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare with alternatives like model_settings_lookup. Usage is implied but not fully clarified.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools cover very diverse domains (weather, FDA, legal, crypto, etc.), so cross-domain confusion is low. However, within domains there is notable overlap: multiple food recall tools (food_recall_check, food_safety), multiple weather tools (weather_current_global, weather_forecast_grid, weather_alerts, weather_bias), and several Polymarket-related tools. This can cause agent misselection.
Naming is inconsistent: some tools use verb_noun (search_arxiv, scrape, validate_agent_manifest), others use noun phrases (smart_money, space_weather, tide_data), and some are long descriptive phrases (cross_platform_arb_scan, polymarket_event_scan). No single pattern is followed, making predictions difficult.
95 tools is excessively high for any coherent purpose. The server appears to be a random aggregation of APIs with no clear scope. Such a large catalog overwhelms agents and dilutes utility; most tools could be split into specialized servers.
Although many domains are touched, each is covered only shallowly. For example, weather lacks historical data, legal lacks case details beyond court opinions, and financial lacks stock prices. There are obvious gaps like no user authentication or data persistence. The tool set feels like a collection of endpoints rather than a cohesive service.