list_supported_banksFree capability check. Lists banks, data freshness, coverage state, and whether get_bank_offers is currently enabled.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool safe (read-only, idempotent, non-destructive). The description adds value by stating this is a 'free' capability check and listing specific outputs (data freshness, coverage state, enabled status), which are useful behavioral details 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 a single, concise sentence that front-loads the key point ('Free capability check') and then enumerates the exact outputs. 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 low complexity (no params, simple list) and rich annotations, the description fully covers what the tool returns and its purpose. It is complete for an agent to understand when to invoke it.
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 tool has zero parameters, so the description does not need to explain any. Per the rubric, zero parameters gives a baseline of 4, and the description adds sufficient context about the tool's outputs to justify this score.
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 banks, data freshness, coverage state, and the enablement status of a sibling tool. The verb 'lists' is specific, the resource is banks plus relevant metadata, and the scope distinguishes it from get_bank_offers.
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 implies a use case: performing a free capability check before using get_bank_offers, especially by indicating whether that tool is enabled. However, it does not explicitly say 'use this before get_bank_offers' or provide exclusions, so it falls short of a full 5.
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 two tools serve completely different purposes: one queries actual bank offer documents, the other provides metadata about supported banks and system status. There is no overlap, so an agent cannot confuse them.
Both tool names follow a uniform verb_noun pattern in snake_case: get_bank_offers and list_supported_banks. The verbs (get/list) and nouns (bank_offers/supported_banks) are clear and consistent.
With only 2 tools, the set is minimal and feels thin for a server claiming to cover 'China Bank Offers Facts'. It is functional but borders on being too limited, with only one core data query tool and one utility/metadata tool.
The server covers the essential read-only workflow: list supported banks to know what is available, then get offers for a specific bank. There are no dead ends for this core use case, though missing features like cross-bank search or offer filtering would improve completeness.