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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds substantial behavioral context: admin-only access, human-only page-view/funnel numbers, exclusion of own-pipeline renders and generic bots via bot_or_own, and a separate llm_crawler breakdown. No annotation contradiction exists.

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 front-loaded with the admin-only gate and the core promise, but it becomes a dense run-on paragraph full of parenthetical detail, and it says 'three real signal sources' while listing more than three items. Bullets or clearer separation would make it easier to parse.

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 zero-parameter report tool with no output schema, the description is unusually complete: it lists all report sections, data sources, metric definitions, and exclusion logic. An agent has enough information to invoke the tool correctly and interpret what it returns.

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?

There are zero parameters and the schema covers that fully, so parameter semantics are not a real burden. The description instead explains what the no-argument call returns, which is the appropriate 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?

The description clearly identifies this as an admin/curator-only report of autario's own traction, and enumerates the concrete signal sources: human reach, MCP/agent usage, signup funnel, activation metrics, and LLM crawler activity. This specificity makes it easy to distinguish from sibling analytics tools like audience_360, seo_360, or ai_visibility_360 without opening their schemas.

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 gives clear context for when the tool is appropriate: a single consolidated traction report for admins/curators, with no arguments required. It does not explicitly mention sibling alternatives or exclusion conditions, so it stops short of the highest score.

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