Traduce una instrucción en lenguaje natural a una estructura/contrato densa y accionable. input=texto. [x402: 0.002 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | instrucción en lenguaje natural |
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 behavioral disclosure burden. It does add one meaningful trait — a per-call cost note ('0.002 USDC on Base, pay-per-use') — which is genuinely useful. However, it does not disclose the output format, whether the call is deterministic/idempotent, any latency or failure behavior, or any side-effects beyond the cost.
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 short, front-loaded with its purpose, and ends with the cost note. Every sentence earns its place; the only redundancy is the 'input=texto' fragment, which repeats the schema's basic info. It is concise without being cryptic.
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 one-parameter, no-output-schema, no-annotation tool, the description captures the general purpose and cost but leaves a material gap: it never defines exactly what a 'contrato denso y accionable' looks like or what output the caller should expect. An agent could call it correctly, but the expected response shape and success/error semantics remain ambiguous.
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 description coverage is 100% — the schema already describes 'input' as an instructuren lenguaje natural and the description merely echoes that with 'input=texto'. No additional parameter context (length limits, languages supported, encoding, example values) is provided, and it does not compensate for what schema already covers.
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 states a specific verb and resource: translate/transform a natural-language instruction into a dense, actionable structure/contract. This is clear enough to distinguish the core operation from ordinary language translation, though it does not differentiate it from the siblings traductor-juridico, contract-draft, or extract-json, and the term 'estructura/contrato' remains somewhat undefined.
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?
Usage is only implied: the tool is for turning a natural-language instruction into a structured/actionable contract. This context is conveyed by the purpose statement itself, but there is no explicit guidance on when to prefer this over contract-draft, compress, extract-json, or traductor-juridico, and no exclusion criteria are stated.
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.