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

Annotations already mark the tool as read-only and non-destructive; the description adds behavioral context beyond that by noting the analysis is pre-computed, AI-generated, and narrative rather than raw numbers. It does not discuss staleness or latency, but the added context is meaningful.

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 sentences, each earning its place: first states what the tool returns, second gives the invocation trigger, third clarifies the relationship to the sibling raw-metrics tool. No filler or repetition.

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 simple one-parameter read-only tool with annotations covering safety and no output schema, the description sufficiently explains the return value and invocation context. The workflow anchor to nevent_get_campaign resolves likely sibling confusion, including with nevent_get_paid_campaign_insights.

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 coverage is 100% and the campaign_id parameter is already described in the schema, including a pointer to nevent_list_campaigns for discovery. The description adds no parameter-specific meaning, 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?

The description uses a specific verb and resource: 'Get pre-computed AI analysis for a specific campaign' and enumerates the output (performance summary, detected anomalies, recommendations). It distinguishes itself from nevent_get_campaign by saying it complements raw metrics with narrative insights.

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 to call this after nevent_get_campaign, gives concrete user-phrase triggers ('how did this campaign perform?' or 'what could be improved?'), and frames it as a complement to raw metrics rather than a replacement. This is clear when-to-use guidance.

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