list_conversations

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

Annotations already declare readOnlyHint=true, so the description's role in behavioral transparency is reduced. It adds some context by stating the return type (list with metadata) but does not disclose further behavioral traits like pagination behavior or time zone handling.

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 front-loaded with purpose and usage, then lists parameters clearly. Minor redundancy in repeating 'optional' for each parameter, but overall efficient for the number of parameters.

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 that an output schema exists (as signaled by context), the description does not need to detail return values. It covers all key aspects: filtering, pagination, and defaults. No obvious gaps for a list endpoint.

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?

Schema description coverage is 0%, so the description fully compensates. It explains each parameter's purpose (e.g., 'Filter conversations by specific agent ID'), specifies defaults and ranges (e.g., page_size 1-100, max_length default 10000), adding significant meaning beyond the bare 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 'Lists agent conversations. Returns: conversation list with metadata.' The verb 'lists' and resource 'agent conversations' are specific and distinguish from sibling tools like 'get_conversation' (singular) and 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?

It explicitly says 'Use when: asked about conversation history,' providing clear context. However, it does not mention when not to use it or suggest alternatives like 'get_conversation' for a single conversation.

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