robots_from_logA robots.txt naming only the crawlers in your log, each with its request count and cost of blocking, plus a warning for any that do not documentably obey it — there the file is a request, not enforcement. Example: log='412 GPTBot/1.2', stance='block-ai-training' blocks GPTBot only.
| Name | Required | Description | Default |
|---|---|---|---|
| log | Yes | The log text. | |
| stance | No | Default block-ai-training. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral nuance, especially that the generated file is 'a request, not enforcement' for crawlers that do not documentably obey robots.txt, and that it reports counts and costs.
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 a single dense but purposeful sentence with a clarifying example. It is not bloated, and the example earns its place by grounding both parameters, though the em-dash phrasing is slightly convoluted.
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 two simple parameters, one required, and no output schema, the description covers the core output contents and provides a concrete example. It could more fully explain each stance enum value, but the essentials for a correct call are present.
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%, so the baseline is 3. The example adds real semantic value by demonstrating the expected log format ('412 GPTBot/1.2') and showing how stance='block-ai-training' maps to blocking only GPTBot, which clarifies parameter interaction beyond the schema definitions.
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 that the tool produces a robots.txt naming only crawlers found in the log, with request counts, blocking costs, and warnings for non-compliant crawlers. This is specific enough to distinguish it from generic log tools, though it does not explicitly name sibling alternatives.
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?
No guidance is given about when to use this tool versus siblings like waf_ruleset_from_log or triage_log. The example illustrates a single invocation but does not state the intended selection context or exclusions.
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.
Most tools have clearly separate deliverables: triage_log is the full per-line report with rollups, summarize_by_operator is aggregate-only, find_impersonators is contradiction-only, and the two config generators produce different artifacts. Some overlap exists because triage_log can also surface rollups and address contradictions, but the descriptions make the boundaries clear.
The names mix conventions: verb+object (find_impersonators, triage_log), verb+preposition+object (summarize_by_operator), and output+from_log (robots_from_log, waf_ruleset_from_log). The from_log suffix is a useful clue, but the set lacks a single predictable naming pattern.
Five tools is well-scoped for a cawler log triage server: one full analyzer, two focused analyzers, and two artifact generators. Each tool has a clear deliverable and none feels like filler.
The workflow from raw log input to analysis, aggregation, Impoersonation detection, and defensive config generation is covered. A minor gap is the absence of a direct prefix-allowlist generator, since the WAF guidance points at allowlisting published prefixes as the real fix for impersonation.