Score Lead

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

Description confirms mutation (lead status update) and idempotency (weighted average of dimensions), adding details beyond annotations (thresholds, breakdown storage). No contradiction with annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true).

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?

Three sentences concisely cover dimensions, weighting, status update, return value, and prerequisite. No redundant words; information is front-loaded.

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 tool complexity (6 dimensions, weighted scoring, status mutation, prerequisite), description covers all essential aspects. No output schema, but return value is described as updated lead with breakdown.

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 full coverage (100%) with detailed description of lead_id (UUID format). Description adds no param-specific info beyond schema, so baseline 3 is appropriate.

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 computes a 6-dimensional qualification score, updates lead status, and stores breakdown, with specific verb 'compute' and resource 'lead'. It distinguishes from siblings like lead_enrich and lead_qualify by detailing the multi-dimensional scoring and status update.

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 explicitly advises to 'Run lead_enrich first for the most accurate industry/size signals', providing a clear precondition. While it doesn't explicitly state when not to use, the context and singleton parameter make usage straightforward.

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