Pipeline Statistics

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=false. Description adds value by detailing the return shape and confirming full-dataset aggregation. No contradictions. Could mention performance or data freshness.

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 sentences, no fluff. Lists return fields compactly and front-loads the core purpose. Every sentence earns its place with zero 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?

Given no output schema, description fully specifies all return fields and their structure. Complexity is low (no params). Use cases are covered. No missing information for an agent to understand when and how to invoke it.

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?

Input schema has zero parameters (100% coverage). Description reinforces 'Takes no input', confirming behavior. With 0 parameters, baseline is 4; description adds no extra param semantics but correctly describes the lack of parameters.

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 clearly states it provides portfolio-wide pipeline analytics across all leads, lists all returned fields, and distinguishes from sibling tools like lead_score (individual scoring) by emphasizing aggregation. The verb 'Returns' and context 'Ideal for dashboards...' firmly establish purpose.

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?

Explicitly states 'Takes no input — always aggregates the full dataset' and recommends uses: 'dashboards, stand-ups, and conversion-rate tracking'. Does not explicitly list when not to use, but the context and sibling tool names (e.g., lead_search for filtered queries) imply boundaries.

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