VERTICAL(health): clinical-data privacy compliance gaps (HIPAA/etc). input=process+framework. B2B: healthtech validates clinical-data handling. [x402: 35.0 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | service 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 disclosure burden. It discloses pay-per-use pricing and the input pattern, and implies the tool identifies compliance gaps, but it does not explain the output format, what happens to submitted data, or any 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 dense and front-loaded: vertical, domain, input pattern, target market, and pricing are all present with no filler. It is highly compressed, but every token carries information.
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 single-input, paid tool with no output schema and no annotations, the description should clarify the deliverable and expected input syntax more fully. It specifies the vertical and input shape, but leaves the output format and exact usage conditions vague.
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 labels the parameter as 'service input', so the description adds real value by stating input=process+framework. This tells the agent what to supply, though it lacks an example or exact format.
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 domain (health), a concrete deliverable (clinical-data privacy compliance gaps, HIPAA/etc.), and a target buyer (B2B healthtech). It is distinguishable from generic compliance tools like compliance-audit or enterprise-compliance, though phrases like 'input=process+framework' are cryptic.
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?
It gives clear context: use this for healthcare vertical, healthtech B2B, and clinical-data handling validation. It does not explicitly enumerate alternatives or exclusions, but the 'VERTICAL(health)' label effectively routes the agent away from general compliance 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.