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Stone Pagamentos MCP

openfinance_list_accounts

Read-onlyIdempotent

Returns accounts for a bank connection: BANK (checking/savings) and CREDIT (credit card) with balance, number, type, subtype, bankData, and creditData. Also returns bank (the brand/connector name like 'Nubank Empresas' — same shown in the dashboard UI) and connector_id. Note: each account's name is the legal entity that issues the account (e.g. 'Nu Pagamentos S.A. - Instituição de Pagamento'), which is not the same as the brand — when referring to the bank in user-facing text, use bank. OMIT item to list accounts across ALL linked banks at once — the response aggregates every connection's accounts into results, each row tagged with its own bank/connector_id/item_id (use this when the user asks for 'my accounts/cards' without naming a bank). Pass item to target a single bank (response carries bank/connector_id/item_id at the root). CREDIT (credit card) balance: its meaning is CONNECTOR-DEPENDENT — some banks report the current open-bill partial, others the full revolving/installment debt — so do NOT treat balance as 'this month's bill'. The open billing cycle is defined by creditData.balanceCloseDate (when it closes) / balanceDueDate (when it's due). For a standardized open-bill amount and total debt that mean the same across connectors, use openfinance_list_credit_card_bills (open_bill + total_pending_debt, derived from PENDING transactions); closed bills come from that same tool's results. A CREDIT row may carry creditData.usedAmount (how much of THIS card's limit the bank reports as consumed) and a balance_notice. balance_notice means balance came back 0,00 while the bank's own payload indicates an outstanding amount — some issuers never fill the card's consolidated balance field. When it is present, do NOT tell the user the card has nothing to pay: read the amount from openfinance_list_credit_card_bills instead. bankData.closingBalance and automaticallyInvestedBalance are provider-reported extras that can LAG right after a connection is first created: the bank may publish the connection as UPDATED before those derived fields converge, so they can briefly carry a stale/phantom value that a force sync (openfinance_force_sync) reconciles. The account's own balance is authoritative — treat those two as hints until they agree with it. May include a provider_incident block when the Open Finance provider has an OPEN incident affecting a bank in this response: balances and credit limits may be unreliable (incomplete or wrong, e.g. a credit limit near 1,00) even with the connection UPDATED, until the provider recovers. Do not present those values as real. May include an identity_notice when the SAME account (same number) arrives via two connections stamped with DIFFERENT owner/taxNumber: in Open Finance those fields reflect each connection's CONSENT HOLDER (e.g. a joint account consented by both holders), so dedupe by account number before summing balances and do not attribute ownership by owner/taxNumber for those accounts.

Bulk support: accepts item_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemNo
typeNo
item_idNo
item_idsNo

Schema Changelog

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

  1. Added
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  8. Removed
  9. Changed2 schema fields changed
    • addedInput schema / properties / item_id
      Added value: +{
      +  "type": "string"
      +}
    • addedInput schema / properties / item_ids
      Added value: +{
      +  "items": {
      +    "type": "string"
      +  },
      +  "type": "array"
      +}
  10. Added
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  12. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Despite annotations already declaring readOnlyHint and idempotentHint, the description adds substantial behavioral context: connector-dependent credit balance semantics, `balance_notice` pitfalls, lagging `bankData`, `provider_incident` unreliability, and `identity_notice` deduplication guidance. These are non-obvious behaviors that an agent needs to avoid misinforming users.

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 long but every paragraph earns its place given the domain complexity and absence of an output schema. It is front-loaded with the core purpose and scoping behavior, though the dense caveat blocks could be tightened for faster parsing.

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?

The description is exceptionally complete for a read-only list tool with no output schema. It covers return shape, field semantics, caveats, error-like notices, batch behavior, and cross-tool routing, leaving no critical gap for an agent to call the tool correctly.

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?

With 0% schema description coverage, the description carries the full burden for parameters. It explains `item` and `item_ids` with concrete usage, and `type` is mentioned via BANK/CREDIT. However, `item_id` is only indirectly referenced in response context and is not explicitly explained as an input parameter.

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 precise verb and resource: 'Returns accounts for a bank connection' and explicitly distinguishes BANK and CREDIT account types. It adds detail about returned fields and the bank/connector identity, making the tool's purpose unmistakable and distinct from sibling tools.

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 explicitly tells when to omit `item` for cross-bank aggregation and when to pass `item` for a single bank. It also routes the agent to openfinance_list_credit_card_bills for standardized open-bill amounts and total debt, and references openfinance_force_sync for reconciling lagging fields.

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

A4.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially within the openfinance_* group (list vs get vs sync vs status). A few potential overlaps exist (e.g., openfinance_list_transactions vs openfinance_list_transactions_by_item, openfinance_get_account_balance vs openfinance_list_accounts), but descriptions are detailed enough to guide correct selection.

Naming Consistency4/5

The openfinance_* tools follow a consistent verb_noun pattern (e.g., openfinance_list_accounts, openfinance_get_item_status). However, non-openfinance tools (authenticate, connect, marketplace, toolkit_info) use a different style, and one tool (openfinance_list_transactions_by_item) breaks the pattern slightly. Overall readable and predictable within the primary domain.

Tool Count3/5

With 25 tools, the count is on the heavy side per the calibration rubric (16-25 feels heavy). The server covers a broad financial data domain, which justifies the number, but it may present a steep learning curve and potential overwhelm for agents.

Completeness4/5

The tool surface is comprehensive for read-only Open Finance data access: accounts, transactions, balances, bills, loans, investments, category management, connection lifecycle, and status monitoring. Minor gaps exist (e.g., no direct payment initiation, no investment transaction creation), but for the stated purpose of data and analysis, coverage is strong.