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

Annotations establish the safety profile (readOnlyHint=false, openWorldHint=true, destructiveHint=false), and the description adds genuinely new context: the call is asynchronous, returns a runId immediately, and requires polling get_discovery_status with type "influencers" for verified results. This is exactly the behavioral detail annotations cannot convey. It stops short of permissions or quota details, so a 4 rather than 5.

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 tightly packed sentences with zero filler: purpose first, workflow second. Every clause carries information an agent needs, and the async caveat is front-loaded before the follow-up steps.

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 single-parameter async discovery tool with no output schema, the description supplies the full lifecycle — what it produces (a runId), how to retrieve results, and how to act on them. Nothing required to invoke or consume it correctly is missing.

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% and the single parameter customerProfile is fully documented in the schema. The description echoes the ICP concept but adds no syntax, format, or length guidance beyond what the schema already states, so the baseline of 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 verb (AI-discover) and resource (LinkedIn influencers) with a precise scoping qualifier (audience matches your ideal customer profile). An agent can distinguish it from find_decision_makers, find_companies, and run_influencer_agent without opening any 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?

Gives clear context for use and names the follow-on tools (poll get_discovery_status, then track_influencer), which routes the agent through the full flow. It does not, however, state when-not to use it or contrast it against run_influencer_agent, which also relates to influencers.

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