ocr-batch
ocr specialized for batch [x402: 0.01 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | pipeline input |
ocr specialized for batch [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?
There are no annotations, so the description carries the full burden of explaining behavior. It does disclose the cost model and payment requirement (0.01 USDC on Base, pay-per-use), which is useful, but it does not describe input expectations, processing semantics, output behavior, or side effects.
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 very short and front-loads the core purpose before the pricing detail. It wastes no words, though it is a fragment rather than a well-structured sentence.
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 tool with no annotations, no output schema, and many closely related siblings, the description is too thin. It leaves unclear what batch means, how to structure the input, what the response looks like, and when to choose this over ocr-invoices, ocr-tables, or vision-batch.
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 description coverage is 100% for the single parameter, so the baseline is 3. However, the schema only says 'pipeline input', which is vague, and the description adds no further meaning about what the input string should contain or how it should be formatted.
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 specific operation ('ocr') and a scope ('batch'), which distinguishes it from generic ocr and document-type-specific variants like ocr-invoices or ocr-receipts. It is not a tautology, though it could be more explicit about what 'batch' means.
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?
'specialized for batch' implies a use condition, but the description gives no explicit when-to-use or when-not-to-use guidance. It does not name alternatives such as ocr, ocr-tables, or vision-batch, leaving the choice between siblings largely to 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.