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?
The description adds substantial behavioral context beyond annotations: it discloses sequential querying with rate-limit spacing and the return format { results, errors } per connection. Annotations already indicate readOnly and idempotent, and the description does not contradict them.
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 a concise three-sentence paragraph with no wasted words. It is front-loaded with the core action and each sentence adds essential information.
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 has one optional parameter and no output schema, the description is complete: it explains the parameter, the querying behavior, and the return structure. No gaps remain for agent understanding.
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 0% description coverage, but the description fully explains the parameter: items is an array of connection selectors (item_id, connector_id, or connector_name) and the behavior when omitted. This adds critical meaning beyond the bare 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 using a specific endpoint. It distinguishes between listing for specific connections (via items) and for all connections, differentiating it from sibling tools like openfinance_get_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 provides clear guidance on using the items parameter: pass an array of connection selectors for specific connections or omit for all. It explains sequential querying with rate-limit spacing. However, it does not explicitly mention when to prefer this tool over alternatives like get_loan_detail.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping purviews—list_transactions vs list_transactions_by_item, get_credit_card_bill vs list_credit_card_bills, and connect vs list_connections vs get_item_status vs provider_status—so an agent could pick the wrong one. The very detailed descriptions disambiguate them, but the tool names alone do not.
The openfinance_ prefix provides a clear family and most members use list_/get_/update_ verbs, but there are deviations like openfinance_provider_status (no verb), openfinance_disconnect_bank, and openfinance_force_sync. Platform tools (authenticate, connect, marketplace, toolkit_info) follow a different, less uniform style.
At 25 tools, this sits at the top of the heavy range. Most Open Finance operations are justified given the breadth of banking data, but overlapping tools and the mega-marketplace tool with many sub-actions make the set feel slightly bloated.
The Open Finance surface covers list/get/update/disconnect/sync for accounts, transactions, credit card bills, loans, and investments, plus provider health, connection search, and platform-level auth, marketplace, version, and feedback. There are no obvious dead ends or missing lifecycle operations.