Converts photos of handwritten notes, journals, or meeting scribbles into clean, organized markdown text while preserving structure like lis [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?
With no annotations, the description carries the burden of explaining behavior. It discloses the conversion behavior, the markdown output, structure preservation, and a pay-per-use cost hint. It does not cover input format requirements, language limits, or failure behavior, so transparency is only partial.
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 sentence is brief and front-loaded, but it is corrupted by the incomplete phrase 'like lis' and an inline pricing bracket that is not cleanly integrated. This suggests truncated or merged metadata, so the structure is not professional or polished.
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 with no output schema, the description covers the core input and output behavior adequately. However, the absence of annotations, vague schema description, and lack of guidance on image format and supported languages leave meaningful gaps for an agent selecting among many OCR variants.
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 is fully described in the sense that the only parameter, 'input', has a description, but that description is a generic 'pipeline input'. The tool description adds meaning by indicating that the input is a photo of handwritten material, yet it omits format details such as URL, path, or base64.
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 uses a specific verb ('Converts') and identifies the exact resource: photos of handwritten notes, journals, or meeting scribbles. The final markdown output is also stated, so the tool's purpose is clear. It does not explicitly contrast with sibling OCR tools, but the handwriting focus is enough to set it apart.
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 clearly establishes the context for use: any time an agent has photos of handwritten content and needs clean markdown output. It does not name alternatives or exclusion conditions, but the use case is specific and naturally differentiates from the many non-handwriting OCR siblings.
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.