summarize_by_operatorAggregate only: who crawled you, how many requests each, what share, which category, and what blocking each would cost. Eats a uniq -c table straight from a shell pipeline. Example: log='412 GPTBot/1.2' returns OpenAI, 412 requests, 100%, ai-training.
| Name | Required | Description | Default |
|---|---|---|---|
| log | Yes | Log text, or a `uniq -c` user-agent table. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only and idempotent, so the safety profile is covered. The description adds useful behavioral context by explaining that it consumes a shell pipeline's `uniq -c` output and showing exactly what one input line produces.
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 compact sentences plus an example—every clause earns its place. The output-field list is front-loaded, and the example at the end concretely illustrates the expected behavior without redundancy.
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 required parameter and no output schema, the description still tells the agent what the tool returns through six output dimensions and a worked example. It could more formally define category or cost semantics, but nothing essential is missing for correctly invoking the tool.
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?
The schema already documents `log` at 100% coverage, setting a baseline of 3. The description adds value by specifying the expected source format ('uniq -c' table) and giving a concrete example that maps input to output.
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 opens with 'Aggregate only' and enumerates the exact result dimensions: operator, request count, share, category, and blocking cost. This gives a precise verb-plus-resource framing that clearly differentiates it from sibling log-analysis tools.
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 a concrete when-to-use signal: when you have a `uniq -c` table from a shell pipeline and need aggregation. It does not explicitly name sibling tools or state when not to use it, but the 'Aggregate only' qualifier provides basic scoping.
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.