OpenRouter

send-feedback

Submit structured feedback on a specific generation the caller made — a category plus an optional comment. Use after a generation had a problem (wrong or incoherent output, latency, formatting, billing, or an API error) so the OpenRouter team can act on it. Requires the generation id, which get-generation and send-message both return. In the comment, include diagnostic context OpenRouter cannot infer from the generation id: what agent you are and your version, the harness you run in (CLI, desktop app, IDE extension, server SDK), the model you intended to call, what you expected the generation to do, and what it actually did (quote the problematic part of the response if relevant).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`request`	Yes	Structured feedback about a specific generation

TDQS

A4.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description explains that this is an action to transmit feedback and that it must reference a generated id, which the team acts on. However, it does not detail side effects, whether a submission can be overdrawn, or any auth/rate-limit implications. Given the annotations are all false and do not carry a safety profile, the description could do slightly more.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately long but every sentence contributes: the first sentence states the action, the second states when to use, the third explains the id source, and the fourth details the comment guidance. It is organized and front-loaded with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives enough context for a correct invocation: what to submit, when to use the tool, which sibling tools supply the required generation id, and how to write a useful comment. With a single nested object and 100% schema coverage, this is complete for a feedback tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully documents the request/category/generation_id/comment. The description adds valuable guidance on what the comment should contain: diagnostic context like 'what agent you are', harness, expected model, and discrepancy. This goes beyond the generic 'optional free-text comment' and helps the agent fill the field meaningfully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Submit structured feedback on a specific generation the caller made'. It also clarifies the content (a category plus optional comment), so an agent can immediately distinguish this from general messaging tools like send-message.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance: 'Use after a generation had a problem... so the OpenRouter team can act on it.' It also identifies where the generation id comes from ('get-generation and send-message both return'). It stops short of naming explicit when-not-to-use conditions or alternative tools for other scenarios.

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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TDQS

A3.9/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count3/5

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.

Completeness4/5

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.

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