Skip to main content
Glama

OpenRouter

by ai.openrouter.mcp

transcribe-audio

Transcribe speech from an audio file to text. Pass exactly one of audio_url (preferred; fetched server-side) or audio_base64. Returns the transcript plus the cost and generation id. This bills the authenticated user. Find STT models via list-models with output_modalities=transcription.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesSTT model slug, e.g. "openai/whisper-large-v3"
formatNoAudio container format. Required with audio_base64; inferred from the URL or Content-Type otherwise.
languageNoISO-639-1 language hint (e.g. "en", "ja"). Auto-detected if omitted.
audio_urlNoHTTPS URL of the audio file to transcribe; fetched server-side (max 25 MB). Preferred over audio_base64.
audio_base64NoBase64-encoded audio bytes, for small clips only. Requires format.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations cover read, idempotency, open-world, and destructive hints, but the description adds behavioral context by stating this bills the authenticated user and returns cost plus a generation id. It also clarifies server-side fetching and model discovery, all without contradicting annotations.

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 three short, information-dense sentences with no filler. It starts with a clear purpose, then moves directly to parameter constraints, billing, and model discovery.

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?

With no output schema, the description still covers the return payload (transcript, cost, generation id). Combined with the annotations and complete parameter schema it gives an agent everything needed to know how to call the tool and what to expect in response.

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 input schema already covers rial parameters with descriptions for model, format, language, audio_url, and audio_base64 at 100% coverage. The description adds the useful 'exactly one' relationship and the preference for audio_url, but most parameter meaning is already structured in the schema, so the description adds limited additional semantic value.

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

Purpose4/5

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

The verb 'Transcribe' and resource 'audio file to text' are explicit and immediately distinguish this from image/speech-generation siblings. It clearly states the main function but does not explicitly name a sibling to differentiate from, so it falls just short of a 5.

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?

The description gives actionable input guidance: pass exactly one of audio_url or audio_base64, prefer audio_url, and use list-models with output_modalities=transcription to find STT models. It provides clear context and prerequisites, though it does not explicitly say when not to use this tool versus a sibling like generate-speech.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All OpenRouter toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources