CONSUMER: detect intent + categories + sentiment. input=message, categorias=optional. B2B: ticket routing, chatbots. [x402: 0.001 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 provided, the description carries the full burden. It discloses the pay-per-use cost (x402: 0.001 USDC on Base) which is behavioral, but it does not mention side effects, rate limits, or safety aspects. The description adds some value but misses important behavioral disclosures.
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 clear sections for consumer use, input format, B2B use, and pricing. Every fragment carries information, and there is no redundancy.
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?
The description is missing key information: there is no output schema or description of return values, the optional 'categorias' parameter is unexplained and not in the schema, and the payment mechanism is not elaborated. An agent would be uncertain about the tool's output and how to use the optional category feature.
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 has one parameter 'input' with vague description 'service input'. The description clarifies that input is a message, which adds value. However, it also mentions 'categorias' as optional, but that parameter does not exist in the schema, creating inconsistency. Schema coverage is 100%, so baseline is 3, but the added semantics are partially confusing.
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 states the tool detects intent, categories, and sentiment, with a specific resource and verb. It also distinguishes between CONSUMER and B2B use cases. It does not explicitly name sibling alternatives, but the purpose is unambiguous.
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 usage context (CONSUMER vs B2B), which implies when it might be used, but it does not explicitly contrast with alternatives like 'sentiment' or 'classify-topic'. There is no when-not-to-use guidance.
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.