List the caller's saved presets (named bundles of model, system prompt, and sampling config created in the OpenRouter dashboard), ordered by most recently updated. Use to discover which presets exist and get their slugs; use get-preset to inspect one preset's full config.
| Name | Required | Description | Default |
|---|---|---|---|
| request | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds meaningful context: only the caller's presets are returned, results are ordered by most recently updated, and returned data consists of slugs rather than full configurations. This goes beyond what the annotations alone reveal.
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 well-structured sentences. The first communicates purpose, scope, and ordering; the second gives direct usage guidance and names the alternative tool. No word is wasted and the most important information is front-loaded.
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?
Given the simple list operation, the description is complete: 'identifies scope, ordering, return content, and the transition to get-preset. Paginated behavior is not mentioned beyond the schema, and there is no output schema, but the tool's purpose and expected usage is still well covered.
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 does not describe limit or offset semantics, but the input schema includes nested descriptions for both. The schema carries most of the parameter burden, so the description adds little at the peveve. The exposed 'request' wrapper itself is not explained, but the parameters may still be discovered from the schema.
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 a specific verb and resource: 'List the caller's saved presets', defines what a preset is, and even notes the ordering. It also distinguishes itself from get-preset by indicating this tool returns slugs rather than full configs.
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?
Explicitly explains when to use it: discover which presets exist and get their slugs. It names the alternative, get-preset, for inspecting a single preset's full config, so the agent receives clear routing guidance.
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.
