Lista as empresas emitentes do ambiente (id, nome, cnpj). O id é o empresa_id exigido nas demais tools (passo de seleção da empresa).
| Name | Required | Description | Default |
|---|---|---|---|
| account | 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 declare readOnlyHint=true and idempotentHint=true. The description adds value by specifying the return fields (id, nome, cnpj) and its role in the workflow, which goes beyond the annotations.
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?
Two concise sentences, front-loaded with the action and key information, zero fluff.
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 simple list operation with no output schema, the description covers the essential purpose, return fields, and integration with other tools. However, it omits any explanation of the 'account' parameter, which would have made it fully complete.
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 only parameter 'account' is optional and completely undocumented. With 0% schema description coverage, the description was expected to explain it but does not, leaving the agent uncertain about its purpose.
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 clearly states it lists issuing companies with id, name, and cnpj, and explicitly connects the id to the empresa_id used in other tools. This is specific and distinguishes it from sibling tools like hivecloud_mdfe_empresas.
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?
Clearly indicates the tool is a prerequisite for other tools ('passo de seleção da empresa'), which implies when to use it. However, it doesn't explicitly state when not to use it or mention alternatives, but the purpose 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.