See which providers serve a given model and at what price, latency, throughput, and data-policy status, to choose routing or debug a slow provider.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already compute readOnlyHint/idempotentHint/destructiveHint, so the description only needs to add context beyond them. It does add content by disclosing exactly what data the list contains (price, latency, throughput, data-policy), which is valuable because there is no output schema.
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 one efficiently packed sentence thatfront-loads the action and resource, then lists the result fields and the intended purpose. Nothing is wasted.
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 read-only lookup with two required string parameters, the description tells the agent what it will receive (provider list plus metrics), which covers the main guidance need. #The lack of output schema is compensated by listing the returned fields; only exact response format/pagination is undeclared.
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 0% according to the provided context signal, and the description does not compensate: it says 'a given model' but never explains the needed author/slug parameters or their format. The nested slug and author fields also receive only the minimal one-line descriptions in the schema itself.
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 names the verb (List), the resource (provenders serving a given model), and the core data (price, latency, throughput, data-policy), which differentiates it from siblings like list-providers and list-models. It states the purpose clearly: to choose routing or debug a slow provider.
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 phrase 'to choose routing or debug a slow provider' gives clear context for when the tool is useful. It does not explicitly name sibling alternatives or when-not-to-use conditions, so it stops short of 5.
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.
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.
