Skip to main content
Glama

ai_transcribe

Read-only

Transcribe audio to text with timestamps using Whisper.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioYesBase64-encoded audio file

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

The description adds some behavioral context by noting the output includes timestamps and that it uses Whisper. However, it does not disclose whether the operation is synchronous, any audio size limits, or how the timestamps are structured. The readOnlyHint annotation indicates no side effects, but the description does not go beyond the annotation to provide deeper insight into runtime behavior.

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 a single, concise sentence that immediately states the action and output. It contains no extraneous information and is well-structured for quick understanding.

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 the simple one-parameter schema and absence of an output schema, the description is largely sufficient for basic invocation. It covers the core purpose and output, but could benefit from noting limitations such as supported audio formats, file size constraints, or synchronous/asynchronous behavior. Nevertheless, it is reasonably complete for a tool of this simplicity.

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 fully documents the single parameter 'audio' as base64-encoded audio, providing 100% coverage. The description does not add any additional parameter-specific information, such as supported formats or maximum file size, so the baseline score of 3 is appropriate.

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 tool's function: 'Transcribe audio to text with timestamps using Whisper.' It uses a specific verb (transcribe), identifies the resource (audio), names the output format (text with timestamps), and the method (Whisper), which clearly differentiates it from sibling tools like ai_speech or translate.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives. The description only states what it does, without mentioning use cases, prerequisites, or exclusions. It does not explicitly compare to related tools like ai_speech, leaving the agent to figure out the appropriate context.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct capability: chat, embeddings, image generation, speech synthesis, transcription, currency exchange, translation, and weather. There is no overlap or ambiguity between them.

Naming Consistency4/5

The five AI tools follow a consistent 'ai_' prefix pattern (ai_chat, ai_embeddings, etc.), but the three utility tools (currency, translate, weather) break this convention, creating a minor inconsistency.

Tool Count5/5

With 8 tools, the server is well-scoped for a multi-purpose AI and utility toolkit. The count is neither too sparse nor overly heavy, and each tool has a clear role.

Completeness4/5

The toolset covers a broad range of AI modalities (text, embedding, image, audio) plus common utilities (currency, translation, weather). Minor gaps exist, such as video generation or web search, but these are not essential for the apparent scope.

Resources