connection_churn_analysis

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 declares '[READ]', explains platform-specific behavior (reconnect-per-operation, maxclients, channels-per-connection ratio), notes grouping by source for attribution, and discloses the snapshot/history reuse pattern. This is substantive behavioral context beyond what schema or annotations could add.

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 efficiently organized: a one-line purpose, platform breakdown, workflow note, Args list, and return shape. Every sentence earns its place, and the structured layout makes it easy to scan. Despite covering a complex tool, it remains under ~150 words with no filler.

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 the tool's complexity (two platforms, three parameter modes, no output schema) the description is remarkably complete. It covers both platform behaviors, parameter semantics, the snapshot/history workflow, and the exact return dict shape including findings with cause/action/evidence. An agent can select and invoke this tool correctly with no missing context.

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 properties have no descriptions (0% coverage), but the Args section adds meaningful semantics: snapshot is 'Injected snapshot (from a prior call's live pull shape); skips the live pull', history is 'A prior snapshot for delta analysis (optional)', and target is 'Broker target name from config; omit for the default.' This fully compensates for the schema's lack of property descriptions.

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?

Description opens with '[READ] Connection/channel churn → cause + action; works on both platforms,' which names a specific resource and clear analytical purpose. It distinguishes itself from sibling tools by calling it the 'flagship churn analysis' and covering both redis and rabbitmq, whereas siblings target platform-specific metrics like memory or latency.

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?

Provides clear workflow guidance: 'Call it once, keep the returned snapshot fields, and pass them back later as history for delta-based churn' and distinguishes modes: 'Pass snapshot for pure analysis, or a target to pull live.' This gives clear context for usage but does not explicitly name when not to use or alternative tools, so it stops short of a 5.

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