is_verified_crawler_ipTest an IPv4 or IPv6 address against every crawler prefix the operators themselves publish, remirrored every six hours. Returns the matching prefix, its source and the crawlers using it; a miss is reported as a miss, not as a forgery. Example: ip='20.171.206.5'.
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | IPv4 or IPv6 address to check. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the bar is lower. The description adds valuable behavioral nuance: ranges are remirrored every six hours, and a miss is reported as a miss rather than as a forgery, which clarifies an otherwise surprising semantic.
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 tight sentences: the action and scope come first, return behavior second, and a concrete example last. There is no redundant wording or repetition of schema content.
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 one-parameter, read-only lookup tool with no output schema, the description says enough: input format, data source freshness, what is returned, and how misses are reported. No critical operational detail appears to be 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 'ip' parameter is already described as an IPv4 or IPv6 address to check. The description reinforces this with an example but adds no deeper semantic detail beyond what the schema already provides.
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 starts with a specific verb ('Test') and resource ('IPv4 or IPv6 address against every crawler prefix'), making the tool's function immediately obvious. It also specifies the return value and includes an example, which fully distinguishes it from sibling tools like list_crawlers or lookup_crawler.
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 clearly frames the intended use case: checking whether an IP belongs to operator-published crawler ranges. It does not explicitly name alternatives or state when not to use this tool, so it stops short of the full when/when-not guidance.
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 action: classifying user-agent strings, verifying IP addresses, listing, looking up, generating robots.txt, and checking data freshness. The explicit cross-reference between classify_user_agent and is_verified_crawler_ip clarifies rather than blurs their boundaries.
Almost all tool names follow a predictable snake_case verb_noun pattern such as classify_user_agent, list_crawlers, and lookup_crawler. whats_changed deviates slightly from the verb_noun style but remains consistent in lowercase snake_case, so the naming is mostly coherent.
Six tools is a well-scoped set for a crawler index service. Each tool covers a distinct need without overlapping or unnecessary additions.
The tool surface covers the full workflow for understanding and managing crawler data: discovery, identification via UA or IP, detail lookup, robots.txt generation, and freshness monitoring. No obvious dead ends or missing operations for the stated purpose.