list_llm_interactions

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

With no annotations, the description carries the full burden and does substantial work: it discloses cursor pagination semantics, that rows are deliberately trimmed (truncated text, omitted per-turn events) to protect context budget, and that page_size is capped at 100. It omits read-only/auth framing, but the trimming and cap disclosures are genuinely useful behavioral context beyond the schema.

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?

Three short, front-loaded units: purpose, filters, then pagination/trimming behavior. No filler, and the most important scoping caveat (trimming) is stated before the escape hatch is offered.

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 list tool with no annotations and no output schema, the description covers filters, pagination, trimming policy, page cap, and where to go for full detail. It does not sketch the shape of a returned row beyond 'trimmed prompt/response with guardrail outcomes', which is the one remaining gap.

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 must compensate, and it largely does: search is defined as substring matching on prompt text, cursor is explained as the returned value for the next page, and page_size is capped at 100. Only agent_id is left without added meaning, a minor gap given the otherwise strong compensation.

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 verb (List) and resource (LLM interactions) with a clarifying gloss (prompt/response pairs) and scope (with guardrail outcomes). It routes the agent away from get_interaction, so it is distinguishable from its nearest sibling without opening either schema.

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 the agent to use get_interaction for full detail on a specific interaction, which is a clear alternative-with-condition. It does not address when to prefer list_llm_sessions or list_roi_interactions, so it falls short of the full when/where-not/alternatives bar.

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