Gera narrativa personalizada com insights financeiros/fiscais para um cliente usando IA (Claude). Retorna texto em linguagem acessível com destaques e próxima ação recomendada.
| Name | Required | Description | Default |
|---|---|---|---|
| periodo | No | Período de referência no formato YYYY-MM (default: mês atual) | |
| empresaId | Yes | ID da empresa para gerar insight | |
| forceRefresh | No | Forçar regeneração ignorando cache (default: false) |
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?
No annotations exist, so the description carries the full burden. It discloses the use of Claude AI and that the output is accessible text with highlights and a recommended next action, which is useful. However, it doesn't mention cache behavior, latency/cost, or potential variability of AI-generated content despite the forceRefresh parameter.
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 two sentences, front-loaded with verb and resource, and contains no redundant filler. Every phrase adds information: narrative, client, AI, and output structure.
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 explains the return type in words, which matters because no output schema exists. Yet it omits when to use, cache behavior, and service dependencies; for an AI generation tool with no annotations, this is adequate but not 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 schema covers all three parameters with descriptions, so baseline is 3. The description doesn't add parameter-level detail, but the schema already documents each field adequately, so no compensation is needed.
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 starts with 'Gera narrativa personalizada...' providing a specific verb and resource. It clearly distinguishes this from sibling tools by focusing on AI-generated textual insights for a client, not on dashboards or list operations.
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?
There is no explicit when-to-use or alternative/exclusion guidance. The description implies the tool is for generating client-specific narratives but never tells the agent when to prefer it over similar report/insight tools like dashboard_executivo or saude_financeira.
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.
Many tools have distinct purposes, but several overlap: 'calcular_cbs_ibs' and 'consultar_aliquota_cbs_ibs' both deal with CBS/IBS rates, 'criar_cobranca' and 'criar_cobranca_pix' create similar things, and multiple metrics tools (dashboard_executivo, saude_financeira, autopilot_regua) could confuse an agent. The reconciliation_* group is well-differentiated, but overall the large, overlapping surface creates ambiguity.
Names mix Portuguese and English inconsistently (e.g., 'agendar_cobranca' vs. 'subscribe_webhook'). Most Portuguese names follow verb_noun, but many English names are noun_verb (e.g., 'reconciliation_execute') rather than verb_noun. Additionally, some names are vague or non-descriptive ('autopilot_regua', 'badges_contadores', 'metricas_escritorio'), breaking any predictable pattern.
61 tools is excessive for most MCP servers, even for a broad financial/fiscal automation domain. The count feels bloated, and several tools (e.g., criar_orcamento, listar_orcamentos) are dead weight being deactivated. This would likely be better split into multiple focused servers (e.g., cobranças, reconciliation, fiscal).
The tool surface covers many workflows: cobranças, NFS-e, reconciliation, tax simulation, dashboards, webhooks, and client insights. However, there are notable gaps in basic CRUD: no update/delete for cobranças or clientes, and no way to manage orcamentos (they're deactivated). The reconciliation module is very complete, but the overall surface has dead ends and missing lifecycle operations.