INDUSTRY REPORT: Fintech en LATAM (deep, cited). input=optional scope. B2B: fintechs e inversores priorizan mercados y estrategias en LATAM. [x402: 120.0 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 must carry behavioral disclosure; it does convey 'deep, cited', optional scope input, and pay-per-use pricing of 120.0 USDC on Base. It does not disclose output format, latency, external access, authentication, or other operational effects, leaving notable gaps.
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 tightly packed and front-loads the core purpose before audience and pricing, with no filler. The use of shorthand fragments ('input=optional scope', '[x402: ...]') sacrifices readability but does not waste words.
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 paid tool with no annotations or output schema, the description is incomplete: it omits expected report structure, citation style, example input values, and the fact that the schema actually requires the 'optional' input. An agent could not reliably determine what to pass or what to expect back.
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 only parameter is documented generically in the schema as 'service input', so the description's 'input=optional scope' adds needed meaning. However, it directly conflicts with the schema's required field, provides no example format, and no hint of what valid scope values look like.
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 a deep, cited industry report focused on fintech in LATAM for B2B fintechs and investors, which differentiates it from generic report siblings. It lacks a direct action verb, and the phrase 'input=optional scope' introduces ambiguity about what the tool does.
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 identifies a target audience (fintechs and investors prioritizing LATAM markets/strategies) but gives no explicit when-to-use guidance, exclusions, or alternatives among the many report/research sibling tools. The reader must infer appropriate usage.
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.