Get full details for one model by author/slug (supports :variant suffixes and slug aliases) without fetching the whole catalog. Use this instead of list-models when the model is already known.
| Name | Required | Description | Default |
|---|---|---|---|
| request | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate readOnlyHint=true, which is consistent with the read-only nature of 'Get full details'. The description adds meaningful behavioral context beyond the annotations: the tool supports :variant suffixes and slug aliases, and it avoids pulling the whole catalog. This is useful operational detail, though the exact return payload is not described.
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 sentences carry the complete usage context: what the tool does, what extra syntax is supports, and when to choose it over the sibling tool. There is no redundant or filler language.
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 single-model lookup tool, the description covers the key context: the resource key, the supported identifier formats, and the relationship to list-models. It does not specify what 'full details' includes, but given the small scope and the absence of an output schema, the description is enough for safe invocation.
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 description adds semantic information to the otherwise sparsely documented top-level 'request' parameter by explaining that identification happens via author/slug and that variants and aliases are accepted. It also contextualizes the nested slug description from the schema by tying it to the tool's purpose.
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 action: 'Get full details for one model by author/slug', identifies the resource type, and immediately differentiates it from list-models by emphasizing it avoids fetching the whole catalog. An agent can quickly understand what this tool does and how it differs from its siblings.
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 explicitly says to use this instead of list-models when the model is already known. This gives a direct when-to-use guideline and names the alternative, leaving no ambiguity about tool selection.
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.
