Value Engine Analysis

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

Annotations already indicate read-only, destructive=false, idempotent, open-world. The description adds value by explaining source parameter behavior (auto/hubspot/sample) and the returned JSON schema, which is not in 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, well-structured, and front-loaded. It uses bullet and argument lists efficiently 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 output schema exists, the description covers the essential aspects: purpose, engine types, parameters, and return format. It lacks only minor details like prerequisites for HubSpot, but overall it's sufficient for an AI agent.

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 compensates by explaining engine_type (three options with details), source (three modes with conditions), and pipeline_id (optional filter). It adds meaning beyond the raw schema fields.

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 analyzes a Value Engine (Growth, Fulfillment, or Innovation) and lists distinct stages for each type. It differentiates from siblings like detect_signals and score_pipeline_health, which serve different purposes.

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 implicitly guides usage by specifying the engine types and return structure, but does not explicitly state when not to use it or compare it to alternatives. Siblings have distinct functions, so confusion is unlikely.

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