List Customers

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

Annotations provide strong behavioral context (readOnly, idempotent, etc.). The description adds valuable context by specifying the data types (KYC/KYB) and the operational scope (tenant's instance), enriching the understanding beyond the structured metadata.

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 poorly structured for an AI agent because it explicitly lists every single parameter ('Args (all optional): ...') when the schema already contains this detailed information. This redundancy makes the definition verbose and less efficient.

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 100% schema coverage and rich annotations (readOnlyHint: true), the definition is highly complete. The description provides the necessary semantic context (KYC/KYB) that complements the technical details in the schema.

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?

With schema description coverage at 100%, the baseline score is 3. The description is primarily repetitive, listing all the parameters again, but does not provide any unique semantic meaning or operational constraints not already present in the structured input 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 resource and action (List customers) and defines the scope (KYC/KYB on the tenant's instance), making it immediately clear what the tool achieves and how it differs from sibling tools like get_customer.

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?

By specifying that the customers are on 'the tenant's instance' and mentioning KYC/KYB status, the description implicitly defines the operational context for its use. While it doesn't explicitly compare it to every sibling, the scope provided is sufficient to guide usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.