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openfinance_search_bank_connectors

Read-onlyIdempotent

Searches the available bank connectors by name (pass keywords[], e.g. ['nubank','btg']) and returns, per match: the connector id, whether it's Open Finance or API (access), PF/PJ (audience), the user's already-linked connections (and accounts when include_accounts=true), and a ready connect_url with the bank pre-selected. Some non-Open-Finance credential connectors carry a caveat warning that they don't auto-update (needs periodic manual reconnection) — surface it so the user can prefer the institution's Open Finance connector for automation. Honors the user's plan (a PF plan hides PJ banks; a PJ plan covers BOTH — PF and PJ banks connect and count under the same plan). Call this BEFORE connecting to hand the user a one-click link to the right bank. keywords[] is REQUIRED — without it returns a hint (never dumps the whole catalog).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNo
include_accountsNo

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

A4.7/5.0
Behavior5/5

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds critical behavior: it returns a connect_url, honors user plan (PF/PJ), and mentions caveats for non-Open-Finance connectors. 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 detailed but not overly long. It front-loads the main purpose and adds necessary caveats. A minor reduction for slightly dense phrasing, but still efficient.

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?

With no output schema, the description thoroughly explains what the tool returns per match, including connector id, access, audience, connections, accounts, and connect_url. It also covers plan behavior and caveats, making it 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?

Schema coverage is 0%, but the description explains that keywords[] is required for search terms and include_accounts is boolean to include accounts. It provides syntactic and semantic context beyond the schema, which only specifies 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 clearly states that the tool searches bank connectors by name, returns specific fields like connector id, access type, audience, and a ready connect_url. It explicitly says to call this BEFORE connecting, distinguishing it from sibling tools like 'connect' or 'openfinance_list_connections'.

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 tells when to use the tool (before connecting, to get a one-click link) and that keywords[] is required. It explains the behavior without keywords (returns a hint). However, it does not explicitly state when not to use it or mention alternatives, but the context is sufficient.

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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