INDUSTRY REPORT: Compliance de IA global (deep, cited). input=optional scope. B2B: legal/compliance de empresas IA planifican cumplimiento global. [x402: 150.0 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?
With no annotations, the description carries the transparency burden. It does disclose that the output is a deep, cited industry report and that it is pay-per-use at 150 USDC on Base. However, it never describes the output format, data sources, or behavior when scope is omitted, and it claims input is optional while the schema marks it required.
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 compact and front-loaded with the core deliverable ('INDUSTRY REPORT'). The fragment style and pricing bracket are terse, but there is no wasted prose.
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-parameter report generator with no output schema, it conveys the topic, depth, citation, target audience, and cost. It is incomplete on what 'scope' can contain, what the returned report looks like, and which jurisdictions/regulations are covered; the optional-vs-required mismatch also leaves an actionable 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 covers the single 'input' parameter 100%, but only describes it as 'service input.' The description adds that it represents an optional scope for the report, which is useful meaning; the 'optional' wording conflicts with the required flag, slightly reducing reliability.
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 the tool produces an industry report on global AI compliance, described as deep and cited, targeting B2B legal/compliance teams. The 'global' scope helps separate it from local compliance siblings, but it never names or directly contrasts them, so it lacks explicit sibling differentiation.
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 a clear context: B2B legal/compliance teams planning global AI compliance use this tool, and the input is an optional scope. It does not say when not to use it or point to alternatives like compliance-audit or enterprise-compliance, so it misses explicit exclusions.
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.