Detalha um MDF-e pelo id: placa do veículo (dadosVeiculo), condutores (nome/CPF), CIOT, documentos vinculados (chaves de CT-e/NF-e), UFs, valor e peso da carga, status e protocolos. A listagem não traz esses campos, só o detalhe. Exige mdfe_tenant_id na conexão.
Bulk support: accepts ids, empresa_ids for batched execution.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| ids | No | ||
| account | No | ||
| empresa_id | No | ||
| empresa_ids | No |
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?
Annotations already cover read-only and idempotent behavior, and the description adds value by clarifying the returned fields (e.g., vehicle data, drivers) and the tenancy requirement. It also notes bulk execution capability. This goes beyond the annotations, providing useful behavioral context without contradiction.
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 exceptionally concise: two sentences in the first paragraph and one in the second. Every sentence serves a purpose: the first list fields and differentiates from listing, the second covers the prerequisite and bulk support. No fluff, front-loaded, and well organized.
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 get-by-id tool, the description provides a complete picture: it lists the output fields, notes the prerequisite, and explains bulk behavior. Combined with annotations that establish safety, this is sufficient. Without an output schema, listing the fields is helpful, though it could also mention response format, but it's not critical here.
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?
With 0% schema description coverage, the description must explain all parameters. It does clarify 'id' as the main identifier and mentions 'ids' and 'empresa_ids' for bulk, but it does not explain 'account' or 'empresa_id' at all. This partial explanation leaves ambiguity for some parameters, so it is only minimally adequate.
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 explicitly states the tool details an MDF-e by id and enumerates the exact fields returned, such as vehicle plate, drivers, CIOT, linked documents, UFs, value/weight, and status. It distinguishes itself from the listing tool by noting that the listing does not contain these detailed fields, and it mentions bulk support, making its purpose unmistakable.
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 by noting that listing lacks the detailed fields, so this tool is for when you need that detail. It also states a prerequisite (mdfe_tenant_id in the connection) and explains bulk support. However, it does not explicitly name alternative tools like hivecloud_list_mdfes, though the implication is clear enough.
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.
The server mixes two distinct domains (HiveCloud fiscal documents and mcp.ai platform controls) with several similarly-named tools (hivecloud_list_ctes vs hivecloud_get_cte vs hivecloud_cte_xml; hivecloud_list_mdfes vs hivecloud_get_mdfe). The generic 'authenticate', 'connect', and 'toolkit_info' overlap in connection/account status purposes, creating ambiguity about which to call for setup.
Tools mostly follow a 'hivecloud_<domain>_<action>' pattern (e.g., hivecloud_cte_emitir, hivecloud_cte_cancelar), but there are exceptions like 'hivecloud_avaliar' (short verb without domain), 'get_cte' vs 'list_ctes' (tense/plural inconsistency), and generic tools 'authenticate', 'connect', 'report_bug' that don't follow the pattern. Mixed Portuguese/English verbs further reduce consistency.
With 33 tools, this is on the heavy side. The domain spans CT-e, MDF-e, NF-e, DC-e, plus platform management (marketplace, toolkit, bug reporting, versioning) — it bundles too many concerns into one server. Several tools (report_bug, show_version, marketplace) feel unrelated to HiveCloud fiscal document handling, making the count feel bloated.
For the CT-e and MDF-e lifecycle, the surface is quite complete: create from NF-e, emit, cancel, edit (carta de correção), delete drafts, print DACTE/DAMDFE, export XML, list/get, and even a travel report. DC-e and NF-e are lighter (only list/query), and missing tools like 'create_mdfe' (no draft creation for MDF-e) or 'update_mdfe' (only cancel/end) are notable gaps. However, core workflows are well covered.