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openfinance_list_transactions

Read-onlyIdempotent

Returns transactions for a bank account (BANK or CREDIT type). For CREDIT (credit card) accounts, this is the ONLY way to get itemized transactions (purchases, subscriptions, etc.). Each credit card transaction MAY carry creditCardMetadata.billId pointing at a bill from openfinance_list_credit_card_bills, but this is a per-connector HINT, not authoritative: some connectors (e.g. Nubank) populate it sparsely (many transactions and installments arrive with no billId) or inconsistently (the same payment tagged to more than one bill). Do NOT reconstruct a bill's total by summing transactions by billId — the bill's own totalAmount from openfinance_list_credit_card_bills is the source of truth. CREDIT PENDING vs POSTED varies by connector: where the bank exposes future-dated status:'PENDING' installments, those represent the OPEN bill plus future bills (future months); where it does NOT, only the last closed bill's POSTED items appear until ~closing. Same query, different coverage per bank (upstream). To get a standardized open-bill total / total debt regardless, use openfinance_list_credit_card_bills (open_bill / total_pending_debt). SCHEDULED (future-dated) ROWS: results are ordered by date DESCENDING, and on a card with long installment plans the TOP of the list is the FUTURE — rows dated months ahead are scheduled installments of purchases already made, not new purchases. Every such row is flagged scheduled:true, the response carries scheduled_count and a notice naming the most recent row that actually happened. NEVER read the first row as 'the latest purchase' without checking scheduled. To list only what already happened, pass to = today. Supports from/to date filters (ISO YYYY-MM-DD) and an optional keyword filter via search_queries (case- and accent-insensitive substring match against description and merchant name, OR semantics across multiple terms). When search_queries is set the tool aggregates up to 5000 transactions within from/to before filtering — narrow from/to if truncated:true is returned. PAGINATION: OMIT both page and page_size (the default) to get ALL transactions in the from/to range in one call — the tool auto-paginates the upstream and returns them under a single logical page (page:1, totalPages:1), up to a 5000 ceiling (truncated:true + warning if exceeded, then narrow from/to). Passing page and/or page_size switches to MANUAL pagination: you get one page (page_size items, default 50, max 500; page defaults to 1) with the REAL total/totalPages, so page_size:5 alone returns the first 5 with totalPages telling you how many pages remain. On upstream errors, returns { total:0, results:[], warning, error } instead of throwing. detail controls how much per-row data you get (default 'compact' = slim, cheap). Use detail:'rich' to enrich each row (when the bank connector provides it) with merchantInfo (estabelecimento: businessName/razão social, cnpj, cnae, category — useful for auto-classifying spending) and extra creditCardMetadata fields: billId (a per-connector HINT toward the transaction's bill — sparse/inconsistent on some connectors like Nubank, so do NOT sum by it to get a bill total; use the bill's totalAmount instead), billForecastDate, cardNumber, purchaseDate, payeeMCC, feeType/feeTypeAdditionalInfo, otherCreditsType/otherCreditsAdditionalInfo. billForecastDate ("YYYY-MM") is the counterpart of billId for the OPEN cycle: PENDING transactions have NO billId (the bank only mints it once the bill closes), so this is the only field telling you which bill a pending purchase will land in — its month OFFSET is per-connector (some banks month+1, others month+0), so surface it as-is and do not derive a due date from it. cardNumber (last 4 digits) separates the primary cardholder's charges from an additional card's when several cards share one account_id. Use detail:'raw' to get the FULL untouched Pluggy transaction object (everything Pluggy returns, un-normalized — heaviest, for when you need a field we don't project). 'rich'/'raw' add tokens per row and coverage varies by bank/Open Finance, so keep the default for normal listings. For the card's statement closing/due dates use openfinance_list_accounts (creditData.balanceCloseDate / balanceDueDate). The response opens with an account echo block ({ account_id, bank, name, number, type, item_id }) identifying WHICH account/bank these transactions belong to. When more than one bank is connected, ALWAYS cross-check the echo against the account you intended to query and name the bank when presenting results — never attribute one bank's transactions to another. If total is 0 for a CREDIT account, check the connection health via openfinance_get_item_status — statusDetail.creditCards.isUpdated: false means the credit card sync failed and a force sync (openfinance_force_sync) or reconnection may be needed. May include a provider_incident block when the Open Finance provider has an OPEN incident affecting a connected bank: transactions may come back incomplete or wrong until the provider recovers, and reconnecting does not fix it.

Bulk support: accepts account_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
pageNo
detailNo
page_sizeNo
account_idYes
account_idsNo
search_queriesNo

Schema Changelog

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

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only declare readOnly/idempotent/non-destructive, which the description fully respects. Beyond those, the description discloses the failure modes an agent can't observe in the schema: auto-pagination vs manual with a 5000 ceiling and truncated:true, date-DESC ordering where future-dated scheduled rows call `scheduled:true`, error-tolerant shapes ({total:0, results:[], warning, error}), per-connector PENDING vs POSTED variability, a provider_incident block, and the caveat that the same query yields different coverage per connector. This is exactly the kind of behavioral context that merits top marks.

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 the paragraphs are scoped and the most important, correctness-critical warnings (billId unreliability, future-date scheduled rows at the top of the list) are front-loaded. The same warning about sparse/inconsistent billId and not summing by bills appears at least two, with the detail-level block re-ratings the domain-specific context already stated earlier — a real redundancy that could be trimmed without loss, which keeps this from a 5.

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 an 8-parameter tool with no output schema and no parameter descriptions, the description is exceptionally complete: it covers filter semantics, two pagination modes, truncation, detail enrichment, zero-error handling, the account echo block for multi-bank safety checks, the account_id bulk path, credit card coverage weirdness, and even remediation steps for a zero total. There is slightly less explicit description of the behavior but the contract 'BANK or CREDIT' plus the shared machinery (ordering, pagination, error shape, echo) is enough to support a correct call for both account types; the credit-specific treatment is the part that genuinely needed amplification and it got it.

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

Parameters5/5

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

Schema description coverage is 0%, so the description carries full responsibility for all 8 parameters, and it delivers: `to`/`from` get ISO YYYY-MM-DD format, `search_queries` gets case/accent-insensitive OR-semantics substring behavior plus the 5000-row pre-filter, `page`/`page_size` get a precise auto-vs-manual pagination contract with defaults and the 500 max, `detail` gets compact/rich/raw semantics with token-cost and coverage notes, and `account_ids` gets the bulk batched note. The description provides richer meaning for every parameter than the bare JSON types.

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: 'Returns transactions for a bank account (BANK or CREDIT type)' and immediately stamps it as the ONLY source of itemized credit card transactions, which distinguishes it from siblings like openfinance_list_transactions_by_item and openfinance_list_credit_card_bills. It clarifies what the tool is NOT for: reconstructing a bill total by summing, pointing instead to the sibling that owns that role. The scope is explicit and non-confusable.

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 gives explicit routing conditions and names real alternatives: openfinance_list_credit_card_bills for standardized open-bill totals, openfinance_list_accounts for closing/due dates, openfinance_get_item_status for zero-total diagnosis, and openfinance_force_sync for sync failure. It also states a negative rule ('do NOT sum by billId') so an agent won't use this tool for a purpose it can't reliably serve, a genuine improvement over a bare CRUD description.

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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