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Resumo financeiro correlacionado

financial_snapshot
Read-only

Retrato único do momento atual (não série): saldo total, receita/despesa média mensal, taxa de poupança, reserva de emergência (meses), endividamento sobre a renda anual e patrimônio. Vem de uma tabela materializada (FinancialSnapshot) recalculada de forma best-effort a cada escrita relevante; se ainda não existir para o usuário, é calculada na hora. Diferença de analytics_history: aqui é 1 número por métrica (o estado agora), lá é série mensal com estatística/tendência.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalDebtNo
totalBalanceNo
savingsRatePctNo
totalEquityCostNo
avgMonthlyIncomeNo
lastCalculatedAtNo
totalEquityValueNo
avgMonthlyExpenseNo
debtToIncomeRatioPctNo
emergencyReserveMonthsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

The annotations declare readOnlyHint=true and destructiveHint=false, and the description builds on that by explaining the data source, best-effort recalculation behavior, and the on-the-fly computation when the snapshot is missing. This adds meaningful behavioral context 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.

Conciseness5/5

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

The description is well-structured and front-loaded: it opens with 'Retrato único do momento atual (não série)', lists the metrics, explains the materialized source, and closes with the key contrast to analytics_history. Every sentence adds distinct value.

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

Completeness5/5

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

With no parameters, a read-only annotation, and an output schema available, the description provides all necessary operational context: it explains what is returned, how freshness works, what happens when the snapshot is missing, and how it differs from the main sibling tool.

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

Parameters4/5

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

The tool has zero parameters, so there is no parameter semantics to document. Per the baseline for a 0-parameter tool, the description appropriately focuses on what the snapshot contains rather than parameter details.

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

Purpose5/5

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

The description clearly identifies a specific verb+resource: it provides a single current snapshot of financial status with a concrete list of metrics. It explicitly distinguishes itself from analytics_history by contrasting '1 number per metric (the state now)' with a monthly series, so an agent can tell them apart.

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

Usage Guidelines5/5

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

The description states exactly when to use the tool: when the current financial state is needed, not a historical series. It names the alternative analytics_history and explains the key distinction, providing both a positive and negative usage condition.

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

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TDQS

A3.7/5.0
Disambiguation3/5

The tools are individually well-described and many cross-reference their closest neighbors, but the set contains several easily confused clusters: create_transaction/confirm_new_transaction, update_equity/add_equity_valuation, the invoice tools (current_invoice, next_invoice, list_pending_invoices, get_invoice), and the many analytics/projection tools. The descriptions help a careful reader, but with 81 tools an agent is likely to misselect among these overlapping surfaces.

Naming Consistency3/5

CRUD operations consistently use create_/list_/update_/delete_ plus a resource noun, and all names are snake_case. However, there is a large second group of noun-phrase analytics tools (cashflow_forecast, spending_projection, categories_insights, transport_routine) plus one-off verbs such as can_afford, pay_invoice, and validate_current_invoices, so the naming convention is mixed even though it remains readable.

Tool Count1/5

81 tools is far beyond the practical MCP tool surface and exceeds the rubric's 50+ extreme-mismatch threshold. Even if each tool maps to a real finance endpoint, the volume overwhelms an agent's context window and makes selection much harder.

Completeness4/5

The server covers the finance lifecycle extensively: accounts, cards, invoices, transactions, recurring rules, budgets, goals, debts, equities, categories, tags, cost centers, profile, projections, and insights all have working read/write paths. Minor gaps remain, such as no update/delete for tags and no direct update/delete for system-generated invoices, but agents can usually work around these.