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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds valuable behavioral context: it is AI-powered, accepts plain English questions, and returns a data table with numbers. It also clarifies that it covers 'any Xmagnet data' with examples, which helps set expectations for scope.

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 suitably structured: an opening sentence states the core purpose, a second sentence defines the output, and the 'Use for:' list provides concrete examples. While the example list is long (nine items), each example earns its place by illustrating different data categories. It is front-loaded with the most important information.

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 the tool's simplicity (one parameter, no output schema), the description is fairly complete. It explains what the tool does, what it returns, and provides examples. It does not mention limitations (e.g., unsupported question types) or execution details like latency, but these are not critical for a read-only analytics tool with a single input.

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 for the single 'question' parameter is 100%, so baseline is 3. The description adds meaning beyond the schema by providing concrete example questions, demonstrating the expected format and breadth of queries. This helps the agent formulate valid inputs.

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 the tool's purpose: 'Run an AI-powered analytics report on any Xmagnet data.' It specifies a verb (run), resource (analytics report), and scope (any Xmagnet data), and distinguishes itself from sibling tools like get_campaign_stats and get_dashboard_stats by emphasizing natural-language questions and broad data coverage.

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 provides clear usage context with a 'Use for:' section listing nine example questions covering various data domains (contacts, campaigns, deals, credits, bounces, unsubscribes). It implies this tool is for ad-hoc analytics questions in plain English, but it does not explicitly state when to prefer alternative specific stats tools over this one, hence not a 5.

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