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pagarme_recipients_write_create_anticipation

Criar/operar recebedores no Pagar.me V5 (só sk_). Ações:

  • create: cria recebedor (data).

  • update: atualiza recebedor (requer recipient_id; data).

  • update_bank_account: atualiza conta bancária (requer recipient_id; data).

  • create_withdrawal: cria saque (requer recipient_id; data: { amount }).

  • create_anticipation: cria antecipação (requer recipient_id; data).

[Flattened action: create_anticipation]

Bulk support: accepts recipient_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
accountNo
recipient_idNo
recipient_idsNo

Schema Changelog

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

  1. First observed

TDQS

C2.5/5.0
Behavior2/5

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

Annotations already indicate readOnlyHint=false (write operation) and destructiveHint=false. The description adds minimal behavioral context: it requires a secret key (só sk_), and notes that the action is a write. No further disclosure on side effects, reversibility, or authentication specifics is provided.

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

Conciseness2/5

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

The description is not concise; it lists multiple actions (create, update, etc.) that are not part of the flattened action, which misleads the agent. The key information (anticipation creation) is buried among extraneous details. A focused description would be more effective.

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

Completeness2/5

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

Given no output schema and a write operation, the description should explain what the tool returns (e.g., anticipation details) and any side effects. It does not cover the return value, the format of 'data', or constraints on anticipation creation. The agent is left with significant gaps.

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

Parameters2/5

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 clarifies that create_anticipation needs 'data' and 'recipient_id', and that 'recipient_ids' enables batch execution. However, it does not explain the structure of 'data' (only given for withdrawal: { amount }), and the 'account' parameter is not mentioned at all. The agent lacks sufficient detail to construct valid input.

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

Purpose4/5

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

The description states it is for creating anticipations on Pagar.me V5, with a flattened action of 'create_anticipation'. It distinguishes from sibling tools like pagarme_recipients_write_create and pagarme_recipients_write_create_withdrawal by focusing on anticipation creation. However, the inclusion of other actions (create, update, etc.) adds clutter and could cause confusion.

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

Usage Guidelines2/5

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

The description mentions that create_anticipation requires recipient_id and data, and bulk support accepts recipient_ids for batched execution. However, it does not provide when to use this tool over siblings (e.g., pagarme_recipients_anticipation_limits) or when not to use it. No explicit usage context or exclusions are given.

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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