OpenRouter

get-endpoint-uptime-history

Read-onlyIdempotent

Get the hourly uptime history of every provider endpoint serving a model over the last 72 hours — the same per-provider uptime timeline shown on the model page. Use it to find which provider degraded during a window (e.g. "model X was failing between 05:00 and 08:30 UTC — whose uptime dipped?").

Input Schema

TableJSON Schema

Name	Required	Description
`to`	No	Optional ISO 8601 end of the window, e.g. "2026-07-23T09:00:00Z"
`from`	No	Optional ISO 8601 start of the window, e.g. "2026-07-23T05:00:00Z". Data covers the last 72 hours.
`slug`	Yes	The model slug, optionally with a variant suffix, e.g. "deepseek-chat" or "deepseek-chat:free"
`author`	Yes	The model author/organization, e.g. "deepseek"

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already reveal readOnlyHint=true, idempotentHint=true, and destructrentHint=false. The description adds useful behavioral context: it returns an hourly timeline of per-provider uptime and mirrors the model-page uptime graph. It does not discuss endpoint rate limits or response structure, but the annotations and use-case context cover the main operator-safety concerns.

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?

Two sentences with no wasted words. The primary behavior is front-loaded, and the usage example is compact and useful. Toose without describing structure or unrelated details.

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

Completeness4/5

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

With no output schema, the description gives enough shape to the response by calling out the per-provider uptime timeline shown on the model page. It does not specify the exact uptime metric fields or status codes, but for a read-only history lookup this is a practical level of completeness.

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

Parameters3/5

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

The schema descriptions cover 100% of parameters, including example values for 'to' and 'from' and clarification that 'slug' can include a variant suffix. The description does not add parameter-specific syntax or semantics beyond that, so the schema already carries the load.

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 states the exact action ('Get'), the resource ('hourly uptime history of every provider endpoint serving a model'), and the time constraints ('last 72 hours'). It is easy to distinguish from the sibling list/read tools because it specifically focuses on uptime history per provider endpoint.

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 explicitly explains when to use this tool: to find which provider degraded during a window, with a concrete example. It does not name alternatives or explicitly state when-not-to-use-it, but no sibling appears to directly overlap with uptime history, so the context is clear.

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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