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Br Consult Nfse Status

br__consult_nfse_status

Verify the current status of a Brazilian electronic service invoice (NFS-e) using its 53-character access key. Provide OAuth2 credentials and select the environment to get the status.

Instructions

Consultar o status de uma NFS-e pelo chave de acesso (chNFSe).

Read-only, não requer confirmação.

[Unverified — endpoint e formato de resposta são inferidos.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scopeNoOAuth2 scope override
tp_ambNoIdentificação do Ambiente (tpAmb): '1' = produção, '2' = homologação2
ch_nfseYesChave de acesso da NFS-e (53 caracteres, formato NFS[0-9]{50})
client_idYesClient ID OAuth2 gov.br
client_secretYesClient Secret OAuth2 gov.br
endpoint_overrideNoURL base do ADN override

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.2

TDQS

A3.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral disclosure burden, and it does so well: it explicitly declares 'Read-only, não requer confirmação' and flags that the endpoint and response format are '[Unverified — endpoint e formato de resposta são inferidos.]'. This is honest, helpful transparency about side effects and reliability.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two short sentences convey purpose, safety characteristics, and an uncertainty warning with no filler. Every piece of text earns its place, and the most decision-relevant information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and all parameters are documented, so the description does not need to explain return values or parameter semantics. The read-only and unverified-endpoint caveats make the description effectively complete for invocation. It could be improved by adding usage context to distinguish it from analogous consult tools, but that gap does not block correct use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the structured schema already documents all six parameters. The description's mention of 'chave de acesso (chNFSe)' does not add meaning beyond the schema's existing description of ch_nfse, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Consultar') and resource ('status de uma NFS-e') and identifies the lookup key (chNFSe). It is clear about what the tool does, but it does not explicitly differentiate it from sibling tools such as br__consult_sefaz_status or br__consult_cte, so it does not quite reach a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives, nor does it mention when not to use it. The read-only and unverified-endpoint notes are behavioral, not usage guidance, so the agent is left to infer the proper selection context.

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