Lists loan contracts per bank connection (GET /loans). Pass items as an array of connection selectors (item_id uuid, connector_id, or connector_name) — one entry per connection to fetch; multiple connections are queried sequentially with rate-limit spacing. OMIT items to list loans across ALL linked banks. Returns { results, errors } per connection.
| Name | Required | Description | Default |
|---|---|---|---|
| items | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing that multiple connections are queried sequentially with rate-limit spacing and the return format '{ results, errors }', which goes beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences, each adding essential information: purpose and endpoint, parameter details, and behavior when omitted along with return format. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one optional parameter, no output schema), the description covers key aspects: purpose, parameter usage, and return structure. It could mention potential limitations like pagination or field details, but it is still complete enough for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one optional array parameter 'items' with 0% schema description coverage. The description fully compensates by explaining that 'items' contains connection selectors (item_id uuid, connector_id, or connector_name) and that omitting it lists all linked banks, providing crucial meaning absent from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists loan contracts per bank connection, specifying the endpoint 'GET /loans'. It distinguishes itself from sibling tools like 'openfinance_get_loan_detail' by focusing on listing multiple loans rather than a single loan detail.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to pass 'items' (for specific connections) and when to omit it (to list across all linked banks). It also mentions sequential querying with rate-limit spacing, but does not explicitly name alternatives when not to use this tool.
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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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.
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.
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.
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.