Reads invoice PDFs or photos and pulls out invoice number, vendor, dates, line items, payment terms, and totals into structured data for acc [x402: 0.01 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | pipeline input |
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool processes PDFs/photos into structured data and mentions the pay-per-use cost ('0.01 USDC on Base'). However, it does not explain output format, authentication/wallet requirements, supported image quality, or failure behavior.
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 a single sentence with no filler, front-loading the core action and extracted fields. The pricing bracket at the end is somewhat awkward but still provides useful information without adding significant noise.
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 one-parameter tool with no output schema, the description conveys the main input types and output fields, which is adequate but not exhaustive. It leaves gaps around how the input should be encoded, whether the tool handles single or batch invoices, and how the structured data is returned.
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 describes the only parameter as 'pipeline input', which is generic. The description adds meaningful semantics by specifying that the input should be an invoice PDF or photo, going beyond the schema and helping an agent populate the 'input' string correctly.
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 names a specific verb ('Reads'), a specific resource type ('invoice PDFs or photos'), and enumerates the extracted data fields ('invoice number, vendor, dates, line items, payment terms, and totals'). This clearly distinguishes the tool from siblings like ocr-receipts and ocr-business-cards by focusing on invoices.
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 description gives clear context: use this tool when you have invoice PDFs or photos. It does not explicitly mention alternative tools or when not to use it, but the invoice-specific phrasing is enough to guide an agent toward the correct use case.
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.