Avalia uma ideia de negócio contra a base da Receita (CNAE + lugar → contagem de ativas). Não estima busca no Google. POST /api/avaliar.
| Name | Required | Description | Default |
|---|---|---|---|
| uf | No | Hint de UF se a frase não tiver cidade. | |
| texto | Yes | Ideia em português, 3–400 caracteres. | |
| municipio | No | Hint de código de município da Receita. |
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 provided, the description carries the full disclosure burden. It discloses the core evaluation logic and the HTTP method (POST /api/avaliar), plus the negative behavior (no Google search estimation). But it does not mention return format, auth requirements, or side effects of the POST call.
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?
Three concise sentences with no filler: the main action, the key limitation, and the endpoint. The most important information is front-loaded.
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?
The description covers the tool's core purpose and key limitation, but with no output schema and no annotations, it leaves gaps around the exact response shape, failure modes, and whether callers need any authentication. The formula hints at the output (count of active businesses) but doesn't confirm it explicitly.
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 the baseline is 3. The description reinforces the relationship between parameters via the 'CNAE + lugar → contagem de ativas' formula, but it doesn't add detail about the hint parameters (uf, municipio) beyond what the schema already provides.
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: 'Avalia uma ideia de negócio contra a base da Receita' and even defines the evaluation formula ('CNAE + lugar → contagem de ativas'). The negation 'Não estima busca no Google' explicitly differentiates it from search-related siblings.
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 gives a clear context for use (evaluating a business idea against the Receita database) and an explicit when-not ('Não estima busca no Google'). However, it doesn't name an alternative sibling that would handle Google search estimation, stopping short of full routing guidance.
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.
Each tool targets a clearly distinct purpose: exact CNPJ lookup, company search, autocomplete, AI filter generation, idea evaluation, reference data, session/watches management, and API utility endpoints. There is no meaningful overlap that would cause an agent to select the wrong tool.
The naming is inconsistent: some tools use verb_noun snake_case (get_cnpj, add_watch, list_watches), some are bare English verbs (search, suggest, contact), and some are bare nouns or abbreviations (health, local, ref, api_index). It also mixes languages, with Portuguese avaliar alongside English names.
13 tools is within a reasonable range for a server covering CNPJ lookup, search, evaluation, monitoring, and API metadata. The count feels slightly broad because several utility endpoints (health, api_index, contact, local) are auxiliary, but no tool is redundant.
The core lookup, search, evaluation, and reference workflows are covered well. However, the monitoring lifecycle is incomplete: add_watch and list_watches exist but there is no remove_watch, and monitor_session has no session status or termination counterpart, creating a dead end for the watch flow.