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PicPay MCP

by io.github.mcp-dir
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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. 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"
+}
  6. Added
  7. Removed
  8. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description adds vital behavioral details: the tool caps at 5000 transactions per account and sets a truncated flag, may include a provider_incident block when data could be incomplete, and describes how granularity affects output (compact vs raw rows). No contradictions with 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 relatively long but well-structured. It starts with the core purpose, then covers parameter details, use cases, and special behaviors. Every sentence adds value, though some Portuguese terms (e.g., 'entradas, saídas') might slightly reduce clarity for English speakers. Overall, it is efficient for the tool's complexity.

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 the tool has 9 optional parameters, no output schema, and is moderately complex, the description is remarkably thorough. It covers the output content for monthly granularity (totals, monthly evolution, top expenses, per-account breakdown), truncation behavior, provider incidents, and batch support. Only minor details like error handling beyond provider incidents are omitted, but overall it is complete for an agent to use 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?

With 0% schema description coverage, the description carries full burden and excels. It explains the purpose and allowed values of key parameters: item accepts connector_id/name/item_id, from/to are ISO dates, granularity defaults to monthly but can be raw, detail enriches rows, type filters BANK/CREDIT, and item_ids enables bulk. This adds significant meaning beyond the raw schema.

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 this tool performs consolidated cash-flow analysis for a bank connection in one call, resolving accounts internally. It distinguishes itself from siblings like openfinance_list_transactions by removing the need to call accounts first. It also provides specific use cases (e.g., 'fluxo de caixa', 'maiores gastos'), making its purpose unambiguous.

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

Usage Guidelines4/5

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

The description explains when to use this tool (for consolidated analysis, avoiding multiple calls) and provides guidance on parameters like granularity and detail. It mentions limitations (5000/account cap, provider incidents) but does not explicitly name alternative tools or state when not to use it. Still, the context is 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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