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Stone Pagamentos MCP

by io.github.mcp-dir

openfinance_list_transactions_by_item

Read-onlyIdempotent

Consolidated cash-flow analysis for a whole bank CONNECTION over a period, in ONE call. Resolves the connection's accounts internally and fans out their transactions, so you do NOT need to call openfinance_list_accounts first nor carry account_id uuids between calls. Pass item (connector_id, connector_name or item_id) to target one bank, or OMIT it to analyze ALL linked banks at once. from/to are ISO dates (YYYY-MM-DD). Default granularity:'monthly' returns a COMPACT summary (no raw rows): total entradas, saídas, saldo_liquido, monthly evolution (por_mes), and top_despesas/top_recebimentos (largest N each), plus a per-account breakdown (by_account). Use this for 'análise anual/mensal', 'fluxo de caixa', 'entradas e saídas', 'maiores gastos/recebimentos'. Set granularity:'raw' to ALSO get every consolidated transaction (heavier — only when itemized rows are needed); combine with detail:'rich' to enrich those rows with merchantInfo (cnpj/cnae/businessName/category) + extra creditCardMetadata (billId, purchaseDate, fees), or detail:'raw' for the full untouched Pluggy object per row, when the connector provides them. type filters BANK or CREDIT accounts. On a connection with many transactions the scan caps at 5000/account and flags truncated:true. May include a provider_incident block when the Open Finance provider has an OPEN incident affecting a connected bank: the totals/rows may be incomplete or wrong until the provider recovers, and reconnecting does not fix it.

Bulk support: accepts item_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNo
fromNo
itemNo
typeNo
top_nNo
detailNo
item_idNo
item_idsNo
granularityNo

Schema Changelog

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

  1. Added
  2. Removed
  3. Added
  4. Removed
  5. Added
  6. Removed
  7. Added
  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
  11. Removed
  12. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds critical behavior: internal account resolution (no need to pass account_id), a scan cap of 5000/account with truncated flag, and a provider_incident block that can cause incomplete/wrong data. It also explains the difference between granularity:'monthly' returning a compact summary vs raw returning all rows. This goes well beyond annotations.

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 well-structured: purpose, parameter details, use cases, then edge cases/limitations. It is front-loaded with the core functionality. While dense, each sentence earns its place; however, the length could be slightly trimmed without losing meaning, so it falls just short of a perfect conciseness score.

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?

Given no output schema and 9 parameters with zero schema coverage, the description is exceptionally complete. It explains return fields (total entradas, saídas, por_mes, top_despesas, by_account), truncation behavior, provider incidents, and bulk support. No significant behavioral gap remains for the agent to invoke correctly.

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 coverage is 0%, so the description compensates fully. It explains item (connector_id, connector_name, or item_id), from/to as ISO dates, granularity (monthly/raw), detail (compact/rich/raw) with concrete enrichment details (merchantInfo, creditCardMetadata), type filter (BANK/CREDIT), top_n (largest N), and item_ids for bulk. Every parameter is semantically defined.

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 explicitly states the tool's function: consolidated cash-flow analysis for a whole bank connection, resolving accounts internally and fanning out transactions. It differentiates from siblings like openfinance_list_transactions and openfinance_list_accounts by noting that it avoids the need to call those first. The verb 'analyze' is clear and the resource scope (connection over a period) is well-defined.

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?

Provides explicit use cases: 'análise anual/mensal', 'fluxo de caixa', 'entradas e saídas', 'maiores gastos/recebimentos'. It also gives guidance on when to use raw granularity ('only when itemized rows are needed') and when to use detail levels (rich vs raw). It mentions that using bulk item_ids supports batched execution, indicating appropriate contexts for that feature.

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.

Resources