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openfinance_get_credit_card_bill

Read-onlyIdempotent

Returns bill-level detail for one or more credit card bills by id (GET /bills/:id): dueDate, billClosingDate (when the cycle closed — the boundary that defines which purchases belong to this bill), totalAmount, financeCharges and payments[] (id, paymentDate, amount, valueType, paymentMode). ITEMIZED PURCHASES (OPT-IN): the bank's bill payload has no transactions in it — they live on the card ACCOUNT. Pass include_transactions:true (plus account_id of the credit card, since the bill itself carries no account reference) and each row also gets transactions[], transactions_count, transactions_sum and reconciles_with_total, already matched to that bill. Always check transactions_basis: bill_id = exact (the bank tagged each transaction with this bill — the normal case for CLOSED bills), date_window = ESTIMATE (confidence:'low', window echoed in transactions_window) used when the connector tags no billId or the bill is still open (PENDING lines get no billId until the cycle closes), unavailable = no link possible. Opt-in because it costs an extra full transaction scan of the account. Whatever the basis, the bill's own totalAmount is authoritative — do NOT rebuild it by summing transactions. Without the opt-in the response carries a transactions_hint; you can also fetch them yourself via openfinance_list_transactions with the credit card account_id and a from/to range ending at billClosingDate. Pass bill_ids as an array — use openfinance_list_credit_card_bills first to discover ids. { results, errors } batch shape. NOTE: Pluggy does NOT return a paid/status field. In Brazilian Open Finance, payments[] reflects payments registered during THIS bill's billing cycle — typically the payment of the PREVIOUS bill (do NOT assume this bill was paid just because payments[] is non-empty). To check paid status, prefer openfinance_list_credit_card_bills which derives payment_status via cross-bill match.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bill_idsYes
account_idNo
transactions_detailNo
include_transactionsNo

Schema Changelog

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

  1. Added
  2. Removed
  3. Changed3 schema fields changed
    • addedInput schema / properties / account_id
      Added value: +{
+  "type": "string"
+}
    • addedInput schema / properties / include_transactions
      Added value: +{
+  "type": "boolean"
+}
    • addedInput schema / properties / transactions_detail
      Added value: +{
+  "enum": [
+    "compact",
+    "rich",
+    "raw"
+  ],
+  "type": "string"
+}
  4. Added
  5. Removed
  6. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Despite readOnlyHint and idempotentHint annotations already covering safety, the description adds critical behavioral context: the bill payload has no transactions (they live on the account), the transactions_basis field explains estimate vs exact matching, the 'do NOT rebuild totalAmount' warning, and the Pluggy payment/status caveat. This goes far beyond annotations and prevents misinterpretation.

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 section adds value: bill-level fields, opt-in transaction behavior, basis semantics, payment caveat, and batch shape. It is front-loaded with the core purpose and then layers details. Slightly long, but each sentence earns its place due to high information density.

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?

For a complex tool with no output schema, this description is extraordinarily complete. It covers return fields, batch shape, transaction linkage methods, estimation confidence, payment interpretation, and alternatives. An agent has everything needed to invoke it correctly and interpret results appropriately.

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

Parameters3/5

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

Schema description coverage is 0%, so the description must carry the full burden. It explains bill_ids, account_id, and include_transactions thoroughly, but completely omits transactions_detail (the enum compact/rich/raw). No definition or guidance for this parameter is given, leaving a meaningful gap for agents trying to use it correctly.

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 states 'Returns bill-level detail for one or more credit card bills by id' with a specific resource and verb. It immediately distinguishes itself from sibling tools by referencing openfinance_list_credit_card_bills for ID discovery and openfinance_list_transactions as an alternative for transactions, so an agent can tell them apart without opening schemas.

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?

Explicit guidance is provided: 'Pass bill_ids as an array — use openfinance_list_credit_card_bills first to discover ids' and 'you can also fetch them yourself via openfinance_list_transactions'. It also specifies when to use include_transactions (opt-in) and the required account_id, making the selection and invocation path crystal clear.

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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