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Glama

transcribe_media

Transcribe any video or audio from a URL or local file into text locally without API keys. Returns the full transcript with optional timestamped segments.

Instructions

Turn any video/audio URL or local file into a transcript, locally and key-free. URLs are fetched with yt-dlp; local files and fetched audio are transcribed with whisper.cpp. Returns the full transcript text plus optional [start --> end] segments.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoPath to a .ggml whisper model, or a model name hint. Falls back to WHISPER_MODEL_PATH.
languageNoLanguage code: 'en', 'zh', 'auto' (default: auto-detect).
translateNoTranslate non-English speech into English (whisper -tr).
timestampsNoInclude [start --> end] segment boundaries (default: false).
url_or_pathYesA http(s) URL to fetch, or a local path to an audio/video file.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: operations occur locally and require no API key, URLs are fetched via yt-dlp, transcription is done via whisper.cpp, and output includes full transcript text plus optional timestamps. This is substantive and gives the agent realistic expectations, though it omits potential failure modes or side effects like temporary downloads.

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?

Three sentences with no fluff: the main purpose is first, then the underlying mechanism, then the return value. Each sentence adds a distinct piece of information an agent needs, making it efficient and well-structured.

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?

There is no output schema, so the description must explain return values, which it does ('full transcript text plus optional [start --> end] segments'). Combined with full parameter coverage in the schema, the agent has enough to invoke the tool correctly. The only gap is lack of guidance on error conditions or limitations, but this is not critical for a straightforward transcription tool.

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 documents all 5 parameters with 100% coverage, so the baseline is 3. The description only indirectly references the timestamps parameter via 'optional [start --> end] segments' and otherwise adds no meaning beyond the schema. That meets the baseline but does not exceed it.

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 clearly states a specific action ('Turn any video/audio URL or local file into a transcript') with a concrete resource and scope. It also differentiates itself by noting it is local and key-free, and the mention of yt-dlp and whisper.cpp distinguishes it from generic siblings by conveying the exact pipeline.

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 clearly defines when to use this tool: for any video/audio URL or local file. It does not explicitly name alternatives or exclusions, but the input scope is precise enough for an agent to recognize this is the transcription tool for both remote and local media, leaving little ambiguity.

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