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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: data source (CF analytics-engine), no PII, caching behavior (5min-1h), and that it's a self-aggregating signal. No contradictions.

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 front-loaded with the core purpose, followed by bullet-style use cases and additional context. It is slightly verbose but each sentence adds value; no 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?

For a tool with one optional parameter and no output schema, the description fully explains what returns (top tools, packs, volume, (pack, tool, count)), caching, and data source. No gaps given the complexity.

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% with a well-described enum parameter. The description adds meaning beyond the schema by explaining the trade-off between shorter and longer windows ('hot right now' vs 'steady-state demand').

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 'Returns the top tools, top packs, and total call volume' with a specific scope (what other AI agents are calling on Pipeworx). This distinguishes it from sibling tools like ask_pipeworx (direct question) and discover_tools (tool discovery).

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?

Three explicit use cases are listed, helping the agent decide when to use this tool (e.g., for trend discovery or canonical tool confirmation). It does not explicitly state when not to use it or name alternatives, but the context is strong enough for decision-making.

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