blocked_traffic_rca

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

No annotations are provided, so the description carries the full burden. It clearly discloses the [READ] nature (safe read), the two operational modes (injected rows vs live pull), and the transformation pipeline (keeps blocked entries, aggregates, ranks, classifies). It also documents the return dict structure precisely. Missing explicit notes on authorization/permission requirements, but for a read-only analytics tool the disclosure is strong.

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 structured and front-loaded with the headline purpose. It is appropriately sized for a 4-parameter tool with 0% schema coverage — the space is needed to document the parameters inline. The Args block is clean and each line earns its place. Slightly verbose but justified given the zero-coverage schema.

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 tool with 4 parameters, no annotations, no output schema, and 0% schema coverage, the description is thorough: it explains the pipeline, modes, and full return shape. It lacks explicit mention of error cases or performance caveats for large log pulls, but the note field in the return dict suggests graceful handling. The degree of completeness is strong for the complexity level.

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 0%, so the description must compensate fully, and it does. 'top' is explained as 'how many source rows to return, noisiest first', 'limit' as 'how many recent log rows to pull when live', 'log_entries' with its injected shape {action, source, destination, destinationPort, protocol}, and 'target' as 'firewall target name from config'. Every parameter gets meaningful semantic context beyond the raw schema.

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 has a clear specific verb+resource ('Rank the noisiest blocked sources and classify cause + action'). It clearly distinguishes itself from siblings like 'top_talkers' (who talk) and 'firewall_log' (raw logs) by specifying it only keeps blocked entries, aggregates by source, ranks, and classifies. The scope is explicit and differentiating.

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 provides explicit usage guidance: 'Pass log_entries for pure analysis, or a target to pull live.' It also explains parameter roles (top, limit, target, log_entries) and their default behavior, plus the two operational modes (analysis vs live pull). This is clear enough to avoid confusion with sibling analysis tools.

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