ocr-structured-json
ocr specialized for structured json [x402: 0.01 USDC on Base, pay-per-use]
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | pipeline input |
ocr specialized for structured json [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?
Annotations are absent, so the description carries the full burden of behavioral disclosure. It does usefully mention pay-per-use and a x402 price, but it does not disclose input requirements, expected output shape, failure modes, accepted file formats, or whether this is a read-only or side-effecting operation.
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 and front-loads the core purpose. The pricing note is compact and adds operational context. It is concise, though it could have spent a few more words clarifying input or output behavior.
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?
With no annotations and no output schema, this description is too sparse for an agent to confidently invoke the tool. There is no indication of what the 'input' is expected to be, what the structured JSON output looks like, or which formats are supported. The meaning of 'structured json' itself is also ambiguous: OCR input that is structured JSON, or OCR output that is structured JSON.
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 parameter 'input' is only described as 'pipeline input', which is unhelpful, and the description does not clarify whether this should be an image URL, base64 data, a file path, or raw text. The description adds minimal meaning beyond the schema.
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 this is an OCR tool specialized for structured JSON, which is a reasonably clear resource + intent and differentiates it from generic OCR siblings like 'ocr' and 'ocr-batch'. However, it does not distinguish it from other structured-output tools such as 'vision-structured-json' or 'destilar-structured-json', so it stops short of a 5.
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 'specialized for structured json' implies when it might be appropriate, but there is no explicit guidance about when to choose this over 'ocr', 'ocr-tables', 'extract-json', or 'vision-structured-json'. No alternative tools or exclusion criteria are mentioned, leaving the selection 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.