Return AI crawler activity for the authenticated client's domain:
total hits, breakdown per bot, most recent hits and last-seen per bot.
Use when an agent needs to assess how frequently AI crawlers visit
a site over the last N days.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description does not need to state safety. The description adds useful context about scope ('authenticated client's domain') and return content, but it does not disclose potential limitations like maximum 'days' value or response pagination. This is adequate but not rich.
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 sentences, no filler. The first sentence states the function and outputs; the second gives the usage guideline. Information is front-loaded and every word adds value.
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?
With one optional parameter, no output schema, and a read-only annotation, the description covers the core purpose and return values. It mentions the domain scope and the output breakdown. It lacks minor details like days range or inclusivity, but for a simple tool this is largely complete.
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 0% for the 'days' parameter, so the description must compensate. It does by explaining the timeframe context ('over the last N days'), which directly maps to the parameter meaning. For a single simple parameter, this is sufficient compensation, though it could be more explicit by naming the parameter directly.
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 'Return AI crawler activity for the authenticated client's domain' with specific outputs listed: total hits, breakdown per bot, most recent hits, and last-seen per bot. This is a specific verb+resource pair that distinguishes it from sibling tools like analyze_geo or get_competitor_actions.
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 includes an explicit 'Use when' clause: 'when an agent needs to assess how frequently AI crawlers visit a site over the last N days.' This provides clear context, though it does not mention alternatives or exclusions, which would be necessary for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action, though analyze_geo overlaps slightly with get_bleed_model and get_competitor_actions. Descriptions clarify the differences, making misselection unlikely.
All tools follow a consistent snake_case verb_noun pattern (activate_artifact, get_bot_activity, mark_applied). Minor deviations like analyze_geo instead of get_geo_analysis don't break the pattern.
16 tools is slightly above the typical 3-15 range, but the server covers multiple subdomains (artifacts, analysis, quick wins, reporting), so each tool has a clear purpose.
The tool set covers the full artifact lifecycle (generate, list, get, approve, activate, regenerate) and the core analysis metrics. Minor gaps like no explicit delete tool for artifacts exist, but regenerate serves that need.