list_conversations

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

No annotations provided, so description carries full burden. Describes output but does not disclose if it's read-only, pagination behavior, or authentication requirements. Acceptable for a simple list but lacks depth.

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?

Concise with two clear paragraphs. Slightly redundant 'Args' section but not excessive. Efficient use of 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?

Covers key aspects: what it does, what it returns, how to use results. Output schema exists, so return values are handled. Adequate for the tool's complexity.

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?

Though schema coverage is 0%, the description adds significant meaning to the single parameter by explaining it as a School ID and directing to list_schools. This goes beyond the schema's type-only definition.

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?

Clearly states it lists message conversations for a school and specifies returned data (IDs, participants, previews). Distinguishes from get_conversation by noting it provides previews only, but does not explicitly differentiate from other list tools.

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?

Explicitly tells when to use get_conversation for full content and provides cross-reference to list_schools for finding school_id. Clear guidance on chaining tools.

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