List Sessions

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context by explaining the grouping mechanism, the OTel semantic convention, and the per-conversation metrics produced. No contradiction with annotations.

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?

Two concise sentences with the primary action front-loaded. The OTel convention explanation earns its place by clarifying why this attribute is meaningful, and there is no redundant restatement of the schema.

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?

With fully documented parameters, rich annotations, and an output schema, the description is nearly complete. It clearly explains the grouping key and intended use case; a brief cross-reference to get_session_stats would make it fully complete.

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 100%, so the schema already documents all five parameters. The description adds no parameter-specific meaning beyond identifying the grouping key, so the baseline 3 applies.

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?

States a specific action and resource: list conversations/sessions grouped by gen_ai.conversation.id, and specifies the surfaced outputs (span counts, token usage, time bounds). This clearly distinguishes it from trace- and span-oriented sibling 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?

The description says it is 'useful for understanding multi-turn conversation activity,' which implies when to use it, but it does not name alternatives or state when not to use it (e.g., versus get_session_stats). Guidance is present but only implied.

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