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WAF ruleset from a log

waf_ruleset_from_log
Read-onlyIdempotent

nginx, Caddy, Cloudflare, HAProxy or Apache rules for only the crawlers in your log. The reply warns that a UA rule stops only an honest client, and that impersonation is an address problem needing the published prefixes as an allowlist. Example: log='412 GPTBot/1.2', target='nginx', scope='ai-training'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
logYesThe log text.
scopeNoA category (default ai-training), 'all-seen', 'impersonators', or a stance.
actionNoDefault block.
targetNoDefault nginx.

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context about the output: the reply warns that a UA rule only stops honest clients and that impersonation needs address-level allowlists. This goes beyond the annotations by disclosing behavioral nuances of the returned content, though it doesn't detail side effects or dependencies.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two sentences and an example. It front-loads the core purpose and then adds a relevant warning about the output. The example is illustrative without redundancy. No filler or excessive detail, making it efficient for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should convey enough about the result. It clarifies that rules are generated and includes a warning, and the example demonstrates expected inputs. However, it doesn't specify the format of the output (e.g., a configuration snippet, a file, etc.) or any prerequisites about the log format. Given the tool's moderate complexity and the presence of annotations, this is adequate but not exhaustive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all four parameters. The description adds a concrete example (log='412 GPTBot/1.2', target='nginx', scope='ai-training') that clarifies usage but doesn't elaborate on parameter semantics beyond what the schema provides. This meets the baseline; no deficiency or exceptional addition.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear purpose: generate WAF rules (for nginx, Caddy, Cloudflare, HAProxy, or Apache) from crawlers identified in a log. It specifies the resource and action. However, it does not explicitly differentiate from siblings like find_impersonators or robots_from_log, though the 'only the crawlers' phrasing implies a distinct scope. The example reinforces understanding.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or provide conditions for choosing between this and sibling tools. The description says 'for only the crawlers' but doesn't clarify scenarios where other tools would be more appropriate. The warning about impersonation hints at use cases but doesn't provide routing guidance.

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

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TDQS

A3.6/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count5/5

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.

Completeness4/5

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.

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