PREMIUM (business): contract/agreement draft from your terms (with legal notice). input=type + terms. [x402: 0.15 USDC on Base, pay-per-use]
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | tipo de contrato y términos clave |
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 transparency burden. It discloses the pay-per-use behavior and cost ('0.15 USDC on Base'), marks it as premium/business-only, and mentions that a legal notice accompanies the draft. It does not detail rate limits, error behavior, or whether the operation is irreversible, but the core behavioral facts are surprisingly clear given the short description.
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 highly compact and front-loads the purpose before specifying the input requirement and pricing. Every segment serves a distinct functional purpose: target audience, output, input format, and cost. A small amount of marketing noise ('PREMIUM') is present, but it does not undermine the description's efficiency.
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 a single simple input parameter and no output schema, the description adequately explains what the agent needs: a contract type plus terms, and the tool produces a draft with a legal notice. It even flags the pay-per-use cost. The only missing contextual detail is the exact output format/structure, but the core call is not impeded.
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 input schema already describes the parameter as 'tipo de contrato y términos clave', and the description repeats this as 'input=type + terms'. Since schema coverage is 100%, the description adds little genuinely new semantic value, though it does reinforce the expected structure of the input.
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 drafts a contract/agreement from the user's terms and includes a legal notice. It names a specific verb and resource ('contract/agreement draft from your terms') and the 'PREMIUM (business)' label indicates target context. It could more explicitly differentiate from sibling tools like smart-document or traductor-juridico, but the core purpose is unmistakable.
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 a usage formula: 'input=type + terms.' This implies the input should contain the contract type and key terms, giving some guidance. However, it does not state when to prefer this tool over any of the many sibling tools nor when not to use it, leaving the agent to infer suitability.
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.