Download a PDF from a URL and return its extracted text. Let an agent read documents (reports, contracts, papers) pay-per-call. input=http(s) URL of a PDF. [x402: 0.003 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | URL de un PDF |
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 full behavioral burden. It does usefully disclose the cost and pay-per-use aspect, and the core read/extract flow. However, it omits important edge behaviors such as what happens with scanned/image-only PDFs, size limits, direct-link requirements, and error handling. The basics are there, but practical caveats are missing.
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 and the functional statement is front-loaded. There is some redundancy: 'pay-per-call' appears twice (in the first sentence and in the cost tag), and listing '(reports, contracts, papers)' could be left out. Overall, it is short and to the point.
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 one-parameter tool, the description covers the main input-to-output flow, and it clearly returns the text. Yet there is no output schema and the description does not mention limitations, such as size limits or scanned PDFs. Since the tool is paid and siblings like OCR exist, a bit more guidance would improve completeness.
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?
The schema has full coverage for the single `input` parameter, and the description refines it as 'http(s) URL of a PDF,' which adds the mode of input. That matches the baseline expectation for 100% schema coverage: the schema already documents the parameter, so the description only adds minor clarity.
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 opens with a specific verb and resource: 'Download a PDF from a URL and return its extracted text.' This clearly identifies the operation as PDF text extraction and, alongside the tool name, distinguishes it from read-url and OCR. It is not ambiguous or tautological.
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 phrase 'Let an agent read documents (reports, contracts, papers)' implies a document-reading use case, and the input is described as a PDF URL. However, the description does not explicitly compare with sibling tools such as read-url for web pages or ocr for scanned documents, leaving the routing decision partly to the agent's inference.
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.