Transcribe an audio file to text (speech-to-text) with LOCAL Whisper, automatic language detection. For agents processing voice notes, calls or podcasts. Upload the audio via multipart or as 'archivo_b64'. Optional 'lang' (es|en|...). [x402: 0.01 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Idioma ISO (es, en, ...) — opcional | |
| archivo_b64 | Yes | Audio en base64 (o subir 'archivo' por multipart) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden and does well: it discloses LOCAL execution, automatic language detection, and the per-use cost (0.01 USDC). Missing details like output format or processing limits, but the critical safety/billing traits are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Four short sentences, each adding a distinct piece of value: what it does, intended use case, upload method, and pricing. No filler or redundant detail keeps it particularly easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema, the description covers the operation, main input, optional language, target users, and cost. It could be more explicit about exactly what is returned (e.g., a plain transcript string), but 'transcribe to text' already strongly implies that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the schema already explains archivo_b64 and lang. The description adds little beyond restating that audio can come via multipart or base64 and that lang is optional, so it doesn't materially enrich parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: it transcribes an audio file to text using Whisper. The phrase 'speech-to-text' and 'automatic language detection' clearly differentiate this from sibling tools such as detect-language or ocr.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It names target use cases — 'agents processing voice notes, calls or podcasts' — giving the agent a clear context for when to select it. It does not explicitly exclude alternatives, but there are no close transcription siblings in the list, so this is a solid context cue.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The set contains many trivially indistinct tools: ai-inference/inference, compress/comprimir, count-tokens/contar-tokens, detect-language/language-detect, and multiple overlapping OCR receipt variants. With 160 tools and pairs that differ only by language or suffix, an agent cannot reliably distinguish several capabilities.
Most names are readable lower-hyphen identifiers, but they mix action verbs, noun phrases, domain prefixes, pipeline suffixes, Spanish/English, and arbitrary demo/batch labels. There is a loose convention, but no consistent verb_noun pattern.
160 tools on one server is an extreme count and clearly unwieldy. Even as a marketplace, exposing every variant, demo, and composed bundle as a top-level MCP tool overwhelms agent selection and adds little distinct capability.
The set covers a huge range of text, image, audio, code, market, compliance, and content-workflow tasks, so many intents have some available tool. However, it is a grab-bag rather than a defined service surface, and the arbitrary demo/specialized variants make it unclear whether a needed operation truly exists or is just a duplicate.