Estatísticas/funil da carteira: quantidade de negócios por mês, quebrada por ramo e por status (com os nomes de ramo e status). Use para um panorama do período ("como está o funil nos últimos N meses").
| Name | Required | Description | Default |
|---|---|---|---|
| periodo_meses | 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, idempotentHint=true, and destructiveHint=false, covering safety. The description adds meaningful behavioral context by specifying the output shape: quantity of deals per month, grouped by branch and status, with names. This goes beyond what annotations provide.
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 sentences, front-loaded with the core function and output details, followed by the use case. Every sentence earns its place, and there is no verbose or redundant content.
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?
Despite having no output schema, the description adequately conveys what the tool returns and when to use it. It covers the single optional parameter's purpose and the nature of the data. Minor details like default period or exact response structure are absent, but they are not essential for selecting and invoking the tool.
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 coverage is 0%, so the description must compensate. It does so by mentioning 'últimos N meses' (last N months), which directly clarifies the single parameter 'periodo_meses' as the number of months to include. The link is inferential rather than explicit, but the meaning is clear from the tool name and description.
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 the tool provides portfolio statistics/funnel: number of deals per month broken down by branch and status, with names. This identifies the specific resource and function. However, it does not explicitly distinguish itself from sibling tools like agger_negocios, relying instead on the aggregate nature of the data.
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 an explicit use case: 'Use para um panorama do período' (use for an overview of the period), and clarifies the intent with 'como está o funil nos últimos N meses'. It does not mention when not to use it or name alternative tools, but the context is clear enough for an agent.
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 distinct function: CEP lookup, quote validation, dashboard stats, account listing, business listing, insurer listing, and separate platform utilities for auth, connectivity, marketplace, bug reports, version, and toolkit state. No two tools overlap in purpose, so an agent can clearly differentiate them.
Tool names mix conventions: some are bare nouns (agger_cep, agger_negocios, marketplace), some use verb_noun (agger_list_accounts, report_bug, show_version), one is noun_verb (agger_cotacao_validar), and several are generic single verbs (authenticate, connect). This inconsistency makes the naming pattern unpredictable and harder to infer.
The 12-tool set is well-scoped: 6 domain-specific Agger tools cover the core insurance broker workflows, and 6 generic platform tools handle authentication, connectivity, marketplace operations, and diagnostics. Each tool earns its place without unnecessary bloat.
The domain coverage is partial: the server supports listing businesses, validating quotes, and viewing insurers, but lacks create/update/delete operations for quotes or detailed single-item views. Some gaps exist (e.g., no create quote, no get_business_detail), but agents can work around them by using the list and validation tools, and the platform utilities address meta-level needs.