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transcribe_telegram_voice

Download and transcribe Telegram voice messages using Whisper AI. Converts voice recordings to text with optional language detection and word timestamps.

Instructions

Download and transcribe a Telegram voice message.

Downloads the voice message from Telegram, transcribes it, then deletes the temp file.

Args: file_id: The file_id from a Telegram voice message (from the Message object). bot_token: Telegram bot token. Falls back to TELEGRAM_BOT_TOKEN env var. language: Optional ISO-639-1 language code. None = auto-detect. word_timestamps: Include word-level timestamps in segments.

Returns: Same dict structure as transcribe_audio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_idYes
bot_tokenNo
languageNo
word_timestampsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: downloading from Telegram, transcribing, and deleting temp files. However, it does not cover aspects like error handling, rate limits, or authentication needs beyond the bot token fallback.

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 well-structured and front-loaded with the core purpose. Each sentence adds value: the first states the action, the second details the process, and the parameter/return sections are clear and necessary. No wasted words.

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?

Given no annotations, 0% schema coverage, and an output schema present, the description is mostly complete. It covers the tool's purpose, process, parameters, and return reference. However, it lacks details on error cases or performance expectations, which could be useful for a tool involving external services.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for all parameters: 'file_id' is explained as from a Telegram Message object, 'bot_token' has a fallback, 'language' specifies auto-detect behavior, and 'word_timestamps' clarifies its effect. This goes beyond the basic schema titles.

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 the specific action: 'Download and transcribe a Telegram voice message.' It distinguishes from sibling tools like 'transcribe_audio' by specifying the Telegram source and mentions the cleanup step of deleting temp files, which adds unique context.

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

Usage Guidelines3/5

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

The description implies usage for Telegram voice messages but does not explicitly state when to use this tool versus alternatives like 'transcribe_audio'. It mentions the fallback to an environment variable for the bot token, which provides some context, but lacks clear guidance on prerequisites or comparisons with siblings.

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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