get_loyalty_balance

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

With no annotations, the description carries the full transparency burden. It discloses the authentication requirement ('requires an authenticated Carrefour session') and specifies the return content (bonus, timestamp, total points, validity date). It does not discuss side effects or failure modes, but for a read-only lookup, this 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 concise and well-structured. It leads with the action, then lists returned fields, then usage context, and ends with the auth requirement. No fluff or redundancy.

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?

For a parameterless tool with no output schema, the description is complete: it states what it does, what it returns, and the prerequisite. It fully covers the necessary information without needing an output 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?

The tool has zero parameters, so per the rubric the baseline is 4. The description does not need to add parameter semantics, and the schema covers all (0) parameters. It adds contextual semantics that are not parameter-specific.

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 verb 'Retrieves' and the resource 'loyalty points balance for the authenticated user on Carrefour France'. It also outlines the specific returned fields, distinguishing it from sibling tools like get_loyalty_cards or get_loyalty_coupon_collection.

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 a use case ('Useful for displaying the user's current loyalty points balance') but does not explicitly compare with alternatives or state exclusions. It implies when to use but lacks guidance on when not to use or which sibling tools to prefer for other loyalty features.

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