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Prévia de alocação de faturas

invoice_allocation_preview
Read-only

Lista transações de cartão cujo período (mês/ano) de fatura calculado hoje diverge do período em que estão realmente alocadas (currentInvoiceMonth/Year vs correctedInvoiceMonth/Year) — útil para detectar faturas desalinhadas antes de corrigir. Não altera nada; é sempre resultado de leitura mesmo não sendo readOnly no schema (nenhuma escrita ocorre nesta rota).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
totalNo

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description's 'Não altera nada; é sempre resultado de leitura... nenhuma escrita ocorre' largely restates that hint rather than adding new behavioral context. The 'mesmo não sendo readOnly no schema' phrasing refers to schema metadata and does not contradict the annotation, but it adds little beyond what the read-only hint already provides.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is compact and front-loaded with the core purpose in the first clause. The safety clause is somewhat redundant given the annotations, but it does not bloat the description significantly.

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 zero parameters, an output schema present, and annotations covering safety, this description is complete for an agent to select and invoke the tool correctly. It explains the detection purpose, the exact comparison being made, and the read-only behavior.

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?

There are zero parameters, so the baseline is 4. The description adds conceptual meaning by naming the relevant fields (currentInvoiceMonth/Year vs correctedInvoiceMonth/Year), which helps the agent understand the output even though no input parameters need documentation.

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 opens with a specific verb and resource: 'Lista transações de cartão' and identifies a precise filtering condition: invoices whose calculated period diverges from the allocated period (currentInvoiceMonth/Year vs correctedInvoiceMonth/Year). This clearly distinguishes the tool from sibling invoice/list tools by its mismatch-detection purpose.

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

Usage Guidelines4/5

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

The description states when this is useful: 'útil para detectar faturas desalinhadas antes de corrigir', giving a clear use case. It does not explicitly name alternative tools or state when not to use it, so it misses the top bar, but the context is unambiguous.

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.