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smart-document

Premium PIPELINE in a single call: takes an image or PDF, extracts its text (OCR), summarizes it and translates the summary to the target language. For agents that need to 'understand a document' end-to-end (invoices, contracts, letters, papers) without orches [x402: 0.03 USDC on Base, pay-per-use]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idiomaNoIdioma destino de la traducción (ISO, def 'en')
archivo_b64YesImagen/PDF en base64 (o subir 'archivo' por multipart)

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the underlying pipeline stages, the input types, the pay-per-use nature, and the cost anchor '0.03 USDC on Base'. It does not cover errors, output constraints, or rate limits, but the core runtime behavior is adequately described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the main behavior: 'Premium PIPELINE in a single call...' The second sentence gives the use case. The parenthetical 'orches [x402: 0.03 USDC on Base]' is slightly awkward and appears truncated, but it does not obscure the meaning.

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?

Despite having no output schema, the description implies the return value—the translated summary—and gives sufficient context for an agent to invoke the tool with a base64 image/PDF and a target language. Minor gaps like size limits and response formatting prevent a perfect score.

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?

Schema description coverage is 100%, so the input schema already explains both archivo_b64 and idioma. The description adds that an image or PDF is accepted and that the document is translated, but does not materially deepen parameter-level semantics beyond the schema. Baseline 3 applies.

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 states a precise action: 'takes an image or PDF, extracts its text (OCR), summarizes it and translates the summary to the target language.' This clearly separates the tool from narrowly-focused siblings like ocr, summarize, or translate-to and makes the pipeline identity explicit.

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 gives a clear context: 'For agents that need to understand a document end-to-end (invoices, contracts, letters, papers) without (orchestration).' This implies when the full pipeline is appropriate. However, it does not explicitly state exclusions, such as 'use ocr or read-pdf for raw extraction only'.

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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TDQS

C2.6/5.0
Disambiguation1/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness3/5

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.

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