health
Saúde da origem e cobertura por UF.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Saúde da origem e cobertura por UF.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
No annotations are present, so the description carries the full burden of behavioral disclosure. The description states only a static data scope and does not say whether this is a read operation, what it returns, how results are structured, or any caveats.
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 and free of filler, but it is under-specified rather than genuinely concise. A single ambiguous noun phrase does not provide enough structured information for reliable tool selection.
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?
With no output schema and no annotations, the description should carry more context. It leaves the meaning of 'origem' and 'cobertura' unclear and gives no indication of expected output or behavior, making it incomplete even for a zero-parameter 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 has zero parameters, so the baseline is 4. The description does not need to explain parameter semantics because there are no parameters to clarify.
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 'Saúde da origem e cobertura por UF' names a resource (health/coverage by Brazilian state) and is not a tautology, but it lacks a verb and leaves 'origem' and 'cobertura' ambiguous. It does not distinguish itself from the nine sibling tools.
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?
There is no guidance about when to use this tool versus alternatives like raio, unidades, or vizinhanca. No exclusions, prerequisites, or routing conditions are provided.
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.
Most tools have clearly distinct purposes: buscar, cep, proximo, raio, empresas, and vizinhanca each target a different lookup mode or data source. Some overlap exists between cep vs unidades (CNEFE points vs units) and empresas vs vizinhanca (by CEP vs by radius), but the descriptions clarify the boundaries.
All names are single lowercase tokens, which gives a uniform visual style, but they mix Portuguese and English (buscar, cep, proximo vs api_index, contact, health) and mix nouns with verbs. The pattern is readable but not fully consistent.
Ten tools is well-scoped for a Brazilian address/CEP/CNEFE/business lookup API. The set covers core lookups, proximity queries, meta endpoints, and support functions without feeling padded or excessive.
The domain is well covered: address search, CEP lookup, CNEFE points/units, nearest point, radius CEP search, business lookups by CEP and radius, plus health and API index. Minor gaps exist, such as no explicit pagination on some endpoints and no direct reverse-geocoding beyond proximity queries, but these are workable.