Dominant Constraint Identification

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

Beyond annotations (readOnlyHint, idempotentHint), the description adds behavioral details: how it analyzes data, the source options ('auto', 'hubspot', 'sample'), and the return structure (JSON with constraint, severity scores, etc.). This provides transparency beyond the schema and annotations.

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 concise and well-structured: a one-sentence purpose, a brief paragraph on analysis, an Args section for parameters, and a Returns section for output. Every sentence adds value 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?

Given the tool's complexity (3 optional parameters, output schema present), the description covers inputs, process, and outputs thoroughly. Annotations handle safety, so no gaps remain. It is fully complete for an agent to invoke correctly.

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 has zero description coverage, but the description explains each parameter in detail: 'source' describes its three modes, 'pipeline_id' is an optional HubSpot filter, 'quota' is an optional revenue quota. This fully compensates for the schema gaps, adding clear meaning.

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 the tool's purpose: 'Identify the dominant scaling constraint bottlenecking revenue.' It explains what it analyzes (pipeline coverage, conversion rates, etc.) and the four possible constraints. This distinguishes it from siblings like 'score_pipeline_health' or 'analyze_engine' which have different scopes.

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 implies usage for revenue bottleneck analysis but does not explicitly state when to use this tool versus its siblings (e.g., 'analyze_engine', 'detect_signals'). No guidance on prerequisites or alternatives is provided, leaving the agent to infer context.

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