Premium PIPELINE in a single call: takes an audio (meeting, voice note, call), transcribes it with local Whisper and writes MINUTES with Summary, Decisions and Tasks. For agents/teams turning a recording into actionable minutes without orchestrating services. [x402: 0.04 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Idioma del audio (ISO) — 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 provided, the description carries the behavioral disclosure burden. It meaningfully discloses local Whisper transcription, structured minutes output, single-call pipeline behavior, and the exact pay-per-use cost of 0.04 USDC on Base. It does not mention upload limits, latency, or exact return format, but the core behavior is transparent.
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?
The description is compact: an opening promise, a brief pipeline explanation, a use case, and pricing. The phrase 'without orchestrating services' overlaps somewhat with 'in a single call,' but the text is front-loaded, scannable, and free of unnecessary depth.
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?
There is no output schema, so the description reasonably compensates by naming the output elements: Summary, Decisions, and Tasks. Combined with a fully documented input schema, the agent knows what to send, what the tool does, and what it returns. The main missing piece is the exact response shape, which is not critical for a simple job-call tool.
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%, so the baseline is 3 even without additional parameter detail. The description adds type-of-audio context like meeting, voice note, and call, but it does not elaborate on the semantics of `lang` or `archivo_b64` beyond what the schema already provides.
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 states a specific verb and resource: it takes audio (meeting, voice note, call), transcribes it with Whisper, and produces minutes containing Summary, Decisions, and Tasks. It also distinguishes itself as a full pipeline rather than a single-stage tool, which separates it from siblings like transcribe and summarize.
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?
The intended use case is explicit: 'For agents/teams turning a recording into actionable minutes without orchestrating services.' This clearly tells agents when to choose this tool. It does not explicitly name alternative siblings or say when not to use them, but the context is strong enough.
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.