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Méliuz MCP

by io.github.mcp-dir

openfinance_list_investments

Read-onlyIdempotent

Returns the investment portfolio for a connection (broker or bank with INVESTMENTS product enabled): FIIs, stocks, ETFs, fixed income (CDB/LCI/LCA/Tesouro), mutual funds, retirement (previdência) and COE. Each row carries balance, amount, amountOriginal, amountProfit, lastMonthRate / annualRate / lastTwelveMonthsRate (when available), dueDate, issuer, ISIN, etc. Returns { total:0, results:[], warning } instead of throwing when INVESTMENTS isn't enabled (403) or other upstream errors. DATA INTEGRITY: when MULTIPLE positions come back as TOTAL_WITHDRAWAL with balance/quantity 0 at once (mass zeroing), the tool cross-checks each position's own transaction history upstream; if the zeroing is contradicted (BUY with no sale/redemption/transfer) the response carries data_integrity_warning and the affected rows are flagged integrity:'suspect_zeroed' — treat those balances as UNAVAILABLE (likely a temporary connector failure publishing zeros), never as real R$0, and do NOT sum them into the portfolio.

Bulk support: accepts item_ids for batched execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemNo
pageNo
typeNo
item_idNo
item_idsNo
page_sizeNo

Schema Changelog

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

  1. Added
  2. Removed
  3. 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"
+}
  4. Added
  5. Removed
  6. First observed

TDQS

A3.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: error handling (returns object with warning instead of throwing 403), data integrity cross-checking logic, and explicit instruction to treat suspect_zeroed balances as unavailable. 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 quite long but well-structured: first sentence states purpose, then details return fields, error handling, data integrity logic, and bulk support. It front-loads the main action. However, it could be more concise by grouping related details and omitting redundant phrases (e.g., 'each row carries...' repeats in the data integrity section).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 6 parameters with no schema descriptions, no output schema, and basic annotations, the description provides rich behavioral context but fails to document parameter usage. The agent understands what the tool does and special edge cases, but cannot reliably construct complete requests without guessing parameter values. The data integrity section adds necessary depth but does not compensate for the parameter gap.

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

Parameters1/5

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

Schema description coverage is 0%, meaning no parameter descriptions in the schema. The tool description does not explain any of the 6 parameters (item, page, type, item_id, item_ids, page_size). Without this, an AI agent cannot correctly fill in the parameters, severely hindering tool invocation. The description focuses on output and behavior, not input semantics.

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 the tool returns an investment portfolio for a connection, listing various asset types (FIIs, stocks, ETFs, etc.). It distinguishes itself from sibling tools like openfinance_list_investment_transactions by focusing on the portfolio overview. The verb 'returns' and specific resource 'investment portfolio' make the 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 the tool (to retrieve investment portfolio data) and provides crucial guidance on handling data_integrity_warning (treat suspect_zeroed rows as unavailable). However, it does not explicitly differentiate use cases from siblings like openfinance_get_account_balance or openfinance_list_transactions, leaving the agent to infer context from the tool name and siblings.

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

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct operation or data type (accounts, transactions, bills, loans, investments, connections, etc.) with no ambiguity. Even closely related tools like openfinance_list_transactions and openfinance_list_transactions_by_item are clearly differentiated by scope and output format.

Naming Consistency3/5

The majority of tools follow the 'openfinance_' prefix for banking operations, but utility tools (authenticate, connect, marketplace, report_bug, show_version, toolkit_info) break this pattern, creating an inconsistent mix. However, the convention is still readable and the utilities are clearly distinct.

Tool Count4/5

25 tools is on the high side but well-justified by the breadth of Open Finance data types (accounts, transactions, credit cards, bills, loans, investments) and supporting operations (sync, status, search, updates). A few tools could potentially be merged, but overall the number is reasonable for the domain.

Completeness4/5

The tool surface covers the core Open Finance workflows: listing, reading details, syncing, updating categories, and checking provider status. Minor gaps exist (e.g., no tool to create or delete accounts/transactions), but these are external constraints. The set enables most user-facing financial queries and actions.

Resources