VERTICAL(edu): full course curriculum with modules+rubric. input=topic+level. B2B: edutechs generate curricula at scale. [x402: 10.0 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 full behavioral burden. It discloses pricing and payment model (10.0 USDC on Base, pay-per-use) and describes the output shape, which is useful. It does not mention authentication, rate limits, or failure behavior, but for a content-generation tool the disclosed cost and output are reasonable.
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 no wasted words. The use of shorthand like 'VERTICAL(edu)' and 'B2B' creates some ambiguity, but every segment earns its place by covering purpose, input, audience, and pricing.
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?
Given one simple string parameter and no output schema, the description covers the essential contract: input shape, output content, target user, and cost. It could benefit from an example or clarification of what 'level' encompasses, but it is complete enough for a competent agent to attempt correct invocation.
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 describes 'input' as 'service input', which is unhelpful. The description adds real semantics by specifying 'input=topic+level', telling the agent exactly what to supply. It does not detail the expected formatting or enumeration of levels, but it meaningfully compensates for the schema's vagueness.
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 tool's output — a full course curriculum with modules and rubric — and indicates the key inputs (topic and level). It stops short of explicitly naming sibling tools or differentiating itself from them, but it is unambiguous in its core purpose.
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 'B2B: edutechs generate curricula at scale' sentence gives a clear intended use case and audience, so an agent can infer when to select this tool. It does not explicitly list exclusions or alternatives, but the context is strong enough for routing.
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.