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

While annotations already declare readOnlyHint, idempotentHint, and destructiveHint, the description adds significant behavioral context: the 5000/account scan cap with truncated:true flag, the provider_incident block indicating potentially incomplete/wrong data, and the note that reconnecting will not resolve incidents. This goes well beyond the structured 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?

Though lengthy, every sentence adds value. The description is front-loaded with the core purpose, then progressively details parameters, use cases, output shape, edge cases, and bulk support. No filler or redundancy; the structure makes a complex tool comprehensible.

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?

Given 9 parameters, no output schema, and no nested objects, the description is remarkably complete. It explains the return structure (total entradas/saídas/saldo_liquido, por_mes, top_despesas, top_recebimentos, by_account), the truncation behavior, provider incidents, and bulk execution. This is fully sufficient for an agent to select and invoke the tool correctly.

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?

With 0% schema description coverage, the description carries full responsibility for explaining parameters. It explicitly covers item (connector_id, connector_name, or item_id; omit for all banks), from/to as ISO dates, granularity (monthly vs raw), detail (compact/rich/raw), type filtering, and item_ids for bulk. top_n is implied via 'largest N each' for top_despesas/top_recebimentos. This is thorough and adds meaning beyond the raw schema.

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 opens with a clear, specific verb+resource: 'Consolidated cash-flow analysis for a whole bank CONNECTION over a period, in ONE call.' It further explains that it resolves accounts internally and fans out transactions, distinguishing itself from openfinance_list_accounts and openfinance_list_transactions by noting that no prior account listing or account_id carry-over is needed.

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?

Explicit usage guidance is provided: 'Use this for análise anual/mensal, fluxo de caixa, entradas e saídas, maiores gastos/recebimentos.' It also clarifies when to choose compact vs raw granularity and when to enrich with detail='rich'. The statement that you do NOT need to call openfinance_list_accounts first clearly positions this tool relative to siblings.

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