list_member_point_rules

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

With no annotations provided, the description carries the burden of disclosing behavior. It explicitly reveals the API endpoint (GET /v1/member_point_rules) implying a read-only operation, and outlines the return structure (dict with total and rules[]). It stops short of discussing authentication, rate limits, or error handling, but for a simple listing tool the level of detail is sufficient.

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 well-structured with clear sections: a one-line summary, purpose, API call, and return structure. Each section adds value without redundancy. The main verb and resource are front-loaded, making it easy to scan.

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 its simplicity (zero parameters, no output schema), the description provides the essential details: what the tool does, the API call, and what the response contains. It names fields like id, name, type, value, and conditions, which is enough for an agent to understand the tool's output. No additional context appears necessary.

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 zero parameters, so the baseline is 4. The description adds no parameter-specific information, and none is needed. It also does not contradict the schema, as the schema remains complete with 100% coverage.

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 it retrieves the store's member point rules settings, with specific examples of rule types (consumption reward ratio, point expiration). This distinguishes it from sibling tools like list_membership_tiers (tiers) or adjust_customer_member_points (modification) since it focuses on rules configuration.

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 notes the intended use cases: analyzing membership loyalty programs and comparing customer point changes. It does not identify when to avoid this tool or mention specific alternatives, but the clear purpose provides adequate contextual guidance.

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