Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds useful behavioral context beyond this: the query is pay-per-query with prepaid credits, credentials are not shared to the platform, and LGPD compliance responsibilities fall on the client. However, notable gaps exist — it doesn't explain what happens if credentials are missing between email/senha vs pkcs12, or whether the query mutates state (a new 'Pedido' implies creation).

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 a single, dense paragraph that covers the essential context — host, data source, payment model, legality, and compliance — without redundancy. It earns high marks for not padding, though it could be restructured to separate the core 'what it does' from the legal and billing context, which would improve scannability.

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 tool with 10 parameters, key security implications, and a pay-per-query model, the description captures the most critical operational context (cost, LGPD compliance, source legitimacy). However, it omits important details like how authentication is chosen between the two credential types, what the output format looks like, and how the credit deduction works in practice. It's complete for the 'why' but incomplete for the 'how' of a complex, high-stakes 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?

Schema coverage is 0%, so the description carried the full burden to explain the 10 parameters — none of which are described in the schema itself. The description adds the payment/credits model, which is helpful for parameter semantics context, and mentions credentials are not the platform's. However, critical questions remain unanswered: which parameters are for credential type (email/senha vs pkcs12), what 'tipo_certidao' values qualify, or how to interpret 'uf' and 'municipio' formats. Given zero schema coverage and a high-stakes payment requirement, this is a significant gap.

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 verb+resource: consulting certificates (certidão) from Registradores/ARISP. It adds meaningful context about the data source (official Brazilian sources) and nature of the query. However, some ambiguity exists between 'Novo Pedido' (new request) and 'consulta' (query), and it doesn't explicitly differentiate from the sibling tools, though context implies distinction from generic tools like 'connect' or 'marketplace'.

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 implies usage context: this is for querying official Brazilian registry data with a pay-per-query model and LGPD responsibilities, which suggests when this would be appropriate. However, it doesn't explicitly state when to use this tool versus alternatives or what the alternatives are in contrast. The LGPD data-controller framing implies a compliance use case but never explicitly states 'use this when you need certificate data from ARISP registrars.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.