VERTICAL(fintech): KYC/AML compliance gaps for a fintech product. input=product+jurisdiction. B2B: fintechs validate a product before launch. [x402: 30.0 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 burden of behavioral disclosure. It does add useful context such as pay-per-use pricing on Base and the pre-launch validation scenario, which helps an agent reason about cost and use. However, it does not describe the output format, scope of coverage, or any limitations.
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 each segment contributes value: vertical, function, input format, use case, and pricing. It is not overly verbose, though the bracketed pricing and VERTICAL tags are somewhat cryptic and could be streamlined.
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 covers domain, input, use case, and pricing, which is a good start. However, since there is no output schema, the agent is left without any description of what the tool returns or how results are presented, which is a notable gap for a compliance-analysis tool.
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's single 'input' parameter is described only as 'service input', so the description adds meaningful semantics by specifying that the input should contain product and jurisdiction. This is a significant improvement over the generic schema field.
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 domain and function: evaluating KYC/AML compliance gaps for fintech products, with an explicit vertical tag 'VERTICAL(fintech)' and target use case. It distinguishes from broad sibling tools like compliance-audit or enterprise-compliance, though it uses a noun phrase rather than a strong standalone 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?
It provides concrete selection context: B2B fintechs validating a product before launch, and specifies the required input shape as product+jurisdiction. It does not explicitly list exclusions or alternatives, but the fintech and KYC/AML focus makes routing reasonably clear.
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.