buscar
Busca textual de logradouro.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Termo com 3+ caracteres. | |
| uf | No | Restringe a uma UF. | |
| cidade | No | Trecho do município. |
Busca textual de logradouro.
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Termo com 3+ caracteres. | |
| uf | No | Restringe a uma UF. | |
| cidade | No | Trecho do município. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / cidade / descriptionAdded value: +"Trecho do município."Input schema / properties / q / descriptionAdded value: +"Termo com 3+ caracteres."Input schema / properties / uf / descriptionAdded value: +"Restringe a uma UF."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Busca textual' implies a read operation, but the description doesn't state what the search returns, whether it's read-only/safe, how results are ordered, or what happens on no match. For a search tool with zero annotation coverage, this is a significant gap.
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?
A single, front-loaded sentence with no wasted words. It states the action and target immediately. It's arguably under-specified rather than verbose, but as pure conciseness this is efficient.
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 search tool with 3 parameters, no output schema, and no annotations, the description is incomplete. It doesn't reveal what the result set looks like, what data source is searched, or any usage context. An agent calling this has no sense of what to expect back, which is a real gap for a retrieval 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 description coverage is 100%, so all three parameters (q, uf, cidade) are documented in the schema. The description's 'textual' maps to the q parameter but adds no syntax, format, or interaction details beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting and the description adds marginal value.
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 'Busca textual de logradouro' uses a specific verb (busca = search) and a concrete resource (logradouro = street address). This distinguishes it from siblings like cep, empresas, and unidades, which target different data types. It's clear, though minimal—it doesn't elaborate on the scope of the search.
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?
No guidance is given on when to use this tool versus alternatives. Siblings like cep (postal code lookup), vizinhanca (neighborhood), raio (radius search), and proximo (nearby) all represent plausible alternative retrieval paths, yet the description never explains the trade-offs or when textual search is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.