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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds valuable context: sequential connection queries with rate-limit spacing and per-connection return structure (`{ results, errors }`). This goes beyond annotations without contradiction.
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 (4 sentences) with a logical structure: purpose and endpoint, parameter usage, omission effect, return format. Every sentence adds value with no redundancy or fluff.
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?
Despite no output schema and a single optional parameter, the description is self-contained: it explains operation, parameter semantics, return structure, and rate-limit behavior. It addresses all needed information for an AI agent to correctly invoke the tool.
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% coverage (no descriptions), but the description fully explains the `items` parameter: it's an array of connection selectors (item_id uuid, connector_id, or connector_name), and omitting it lists all banks. This adds essential meaning that the schema lacks.
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 from siblings like openfinance_get_loan_detail by emphasizing multi-connection listing, making its purpose specific and unambiguous.
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 explains two usage modes: with `items` for specific connections or omitted for all banks. While it provides clear guidance on how to use the tool, it does not explicitly mention when not to use it or suggest alternatives like openfinance_get_loan_detail for individual loan details.
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.
The openfinance_* tools are mostly distinct, but several have overlapping boundaries: openfinance_list_transactions vs openfinance_list_transactions_by_item, openfinance_get_item_status vs openfinance_provider_status, and openfinance_list_accounts carrying balance vs openfinance_get_account_balance. The platform-level tools also blur somewhat: marketplace, toolkit_info, and connect all touch installed or available services.
The openfinance_* group follows a consistent list/get/update verb pattern in snake_case, but platform-level tools like authenticate, connect, marketplace, report_bug, show_version, and toolkit_info break that convention. The set is readable, but it is split between two clearly different naming styles.
At 25 tools, the server is at the heavy end, and several are platform/MCP-management helpers rather than core banking capabilities. The count is not absurd for a broad Open Finance + marketplace adapter, but it feels more cluttered than a focused financial data toolset should be.
The Open Finance surface is thorough: accounts, transactions, balances, credit card bills, loans, investments, connection lifecycle, sync, provider status, and category correction are all covered. The main gaps are the absence of a direct connect/create action (only URLs are returned) and no write operations beyond category updates, but for a read-centric financial data adapter this is close to complete.