OCR: extract text from an image (JPG/PNG) or a PDF, including scanned PDFs with no text layer. Local engine (Tesseract 5), Spanish and English. Upload the file via multipart (field 'archivo') or send it as 'archivo_b64' in JSON. Optional 'lang' (spa|eng|spa+en [x402: 0.004 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | spa|eng|spa+eng | |
| archivo_b64 | Yes | Imagen/PDF 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 transparency burden. It discloses the local Tesseract engine, supported languages, file types, and pay-per-use cost. But the pricing note is cryptic ('x402: 0.004 USDC on Base') and it omits output format, limits, or error/banning conditions.
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 definition is compact and mostly front-loaded with the core purpose. It packs several details into one dense sentence, but no clause is wasteful. The pricing tag is awkwardly embedded but not long enough to break the structure.
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, the description covers file types, mandatory upload mechanisms, language options, and billing. Gaps remain: no mention of the output format, potential size/per-page limits, or how the multipart path reconciles with the schema-required archivo_b64 field.
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 coverage is 100%, so the baseline is 3. The description does add useful intent about base64 vs multipart and language options, but it introduces an undocumented 'archivo' field that is absent from the input schema and writes 'spa+en' against the schema's 'spa+eng', creating potential ambiguity.
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 clear verb and resource: OCR extracts text from images and PDFs, including scanned PDFs. It is distinct from sibling tools but does not explicitly differentiate itself from alternatives like read-pdf or vision.
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 provides concrete input instructions: multipart field 'archivo' or JSON 'archivo_b64', plus optional 'lang'. However, it does not state when to prefer this tool over related siblings such as read-pdf or transcribe, leaving usage selection mostly implied.
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.