INDUSTRY REPORT: Adopción de IA en enterprise (deep, cited). input=optional scope. B2B: líderes enterprise construyen su roadmap de IA con evidencia. [x402: 200.0 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 burden. It discloses that the tool produces a deep, cited report and charges 200.0 USDC on Base, which is useful. However, it does not explain the report format, how citations are generated, or any side effects beyond the pay-per-use model.
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-loads the report type, with no padding. Each fragment adds something: content type, depth, input role, audience, evidence focus, and pricing. The telegraphic style is slightly fragmented but not wasteful.
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 report-generation tool with no output schema, the description gives a reasonable high-level picture but leaves important gaps: it never says what values the scope input can take, what the returned report looks like, or how the x402 payment affects invocation. An agent could call it, but not with full confidence about expected input and output.
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%, but the schema's description is just 'service input,' adding no meaning. The description adds that input is an optional scope for the report, which helps. Yet this conflicts with the schema declaring input as required, and 'scope' remains underspecified.
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 clearly identifies the deliverable as an industry report on enterprise AI adoption, with depth and citations. It distinguishes this tool from siblings like ai-agents-market-2026 or deep-research-report by naming the specific enterprise adoption angle and B2B use case, though it lacks an explicit verb.
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 provides clear context: the report is for B2B enterprise leaders building an AI roadmap, and the input is described as an optional scope. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5.
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.