See which APPS/products drive the most OpenRouter traffic, filterable by category, to gauge ecosystem adoption and find example use cases. For model rankings use list-daily-model-rankings instead.
| Name | Required | Description | Default |
|---|---|---|---|
| request | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish readOnlyHint=true and idempotentHint=true, so the description does not need to restate safety. It adds some context about the returned concept (traffic per app, filterable by category) but does not disclose extra behaviors such as date default, window clamping, or result shape. That is a moderate gap, so 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences, front-loaded with the core function, and ends with the specific sibling alternative. Every clause earns its place and no repeated information is present.
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?
The description gives a clear use case and search and points to the relevant alternative. The structured schema carries detail for defaults/enums, while the description handles high-level intent; given no output schema, one could wish for return-format hints, but the high-level usage remains complete and navigable for a read 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?
The schema_description_coverage is 0%, so the description must compensate for parameter gaps but only mentions 'filterable by category' and does not mention sort, limit, offset, or date. The nested schema has rich docs, but the tool description itself does not add sufficient parameter-level guidance beyond what a user might guess. This undercovers the nested parameters.
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 states the specific verb and resource: 'See which APPs/products drive the most OpenRouter traffic' and clarifies the optional category filter. It also names the sibling alternative, list-daily-model-rankings, making the purpose unambiguous and differentiated.
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?
It explicitly tells the agent when to use this tool (to gauge ecosystem adoption and find example use cases) and when not to use it ('For model rankings use list-daily-model-rankings instead'). This both sets the context and provides a clear alternative.
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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Most tools are clearly differentiated by resource and action: the eight list-* tools each target a distinct view (models, endpoints, rankings, apps, providers, presets, tasks, benchmarks), and cross-references between them reduce mis-selection. A few mild boundaries exist—list-models and list-benchmarks both include benchmark data, and install-ori-harness vs spawn-ori-eval are both Ori recipe tools—but their detailed descriptions mostly resolve these.
The naming is overwhelmingly consistent with a verb_noun pattern using the same prefix set: generate-, get-, list-, send-, along with install-, spawn-, search-, and transcribe-. The only deviation is ping, which is a standard bare health-check tool and does not follow the verb_noun convention.
At 22 tools, the set feels heavier than the ideal 3-15 range, though each tool is arguably purposeful given the broad surface: model catalog, rankings, benchmarks, presets, generation, audio, image, docs, uptime, credits, and Ori workflows. The variety justifies the size to some extent, but the sheer number puts it in borderline territory.
Core workflows are well covered: model discovery (get-model, list-models, list-model-endpoints), generation (send-message, generate-image, generate-speech, transcribe-audio), observability (get-credits, get-generation, get-endpoint-uptime-history), and docs. Notable gaps include no create/update/delete for presets and no persistent provider configuration methods, but these are workable since presets are dashboard-managed and providers can be pinned per request.
