Analyze AI Value Vectors

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

No annotations provided, so description carries full burden. It discloses that output is 'directional, from synthetic/self-reported inputs, not an audited figure' and that it's a free sandbox estimate. Also mentions it's an older naming convention, adding full transparency about behavior.

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 with a parenthetical clarification. Front-loaded with purpose, then usage and limitations. Every part adds value, though slightly verbose with the renaming note. Efficient overall.

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 full schema coverage, no output schema, and clear description of the estimate's nature, the tool is fully specified. Sibling tools are named and differentiated. The description covers all necessary context for an agent to decide when and how to use 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?

Schema description coverage is 100%, with detailed descriptions for each parameter (e.g., departmentType maps loosely-named teams). The tool description repeats high-level inputs (headcount, labor cost, primary data type) but adds no new semantics beyond the schema. Meets baseline but doesn't exceed.

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 explicitly states the tool 'estimates where AI/software value is most likely leaking out of a single department' using specific inputs. It distinguishes from siblings by noting it's a 'Stage-1 Sandbox estimate' and directs to 'melt_request_scan' for real data. Also clarifies it goes by older names like 'AI ROI leverage'.

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?

Provides clear when-to-use: 'when a tech leader asks where value is being lost ... before any real data integration exists.' Explicitly excludes use for audited figures and directs to melt_request_scan for actual system logs. Also explains the output is directional and not audited.

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