Scans receipt photos and extracts merchant, date, line items, tax, tip, total, and currency into structured JSON ready for expense reports o [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 present, so the description carries the full burden. It discloses the pay-per-use cost (0.01 USDC on Base), the structured JSON output, and the extraction behavior. It could add input format requirements or failure behavior, but core operational traits are covered.
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?
A single sentence that front-loads the action and output, with pricing appended. The trailing fragment 'reports o [x402: ...]' appears truncated or typo-laden, slightly reducing polish.
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?
Given the low complexity (one input parameter) and no output schema, the description adequately covers the return shape by listing extracted fields and the expense-report use case. It lacks explicit input format details (URL, base64, file path), but the tool is simple enough that this is a minor gap.
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 only describes the single parameter as 'pipeline input' (100% coverage but generic). The description adds that the input is a receipt photo, clarifying what should actually be passed to the pipeline.
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?
States a specific action ('Scans receipt photos'), resource (receipts), and enumerates extracted fields (merchant, date, line items, tax, tip, total, currency) into structured JSON. This clearly differentiates it from sibling OCR tools focused on invoices, business cards, or handwriting.
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 establishes a clear use case—processing receipt photos for expense reports—so an agent can infer when to choose this tool. It does not explicitly list exclusions or name alternatives such as ocr-invoices or ocr-business-cards, but the context is strong enough for routing.
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.