clarify_requirements

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

With no annotations, the description carries the full burden. It discloses the output nature ('structured, unambiguous specification') and that it flags missing requirements, but says nothing about whether it interacts with the user, whether it mutates state, or what happens when the input is already complete. Partial behavioral disclosure.

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 tight sentences with the analysis action and its enumerated outputs front-loaded; no filler. Slightly dense enumeration but nothing wasted.

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?

An output schema exists, so return values need not be described. However, for a tool sitting in a multi-stage optimization pipeline, the description omits its place in the workflow and how its output feeds formulate_math_model, leaving a gap an agent must guess at.

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% for the single string parameter, so the description must compensate. It does clarify the parameter's content ('raw user optimization problem descriptions'), which is useful, but adds no format, length, or example guidance beyond that.

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?

States a specific verb (analyzes) and resource (raw user optimization problem descriptions) plus the concrete artifacts it extracts (constraints, sets, parameters, variables, bounds, objectives). It does not, however, explicitly distinguish itself from near-neighbors like review_requirements or decompose_optimization_query, which a reader must infer from the names alone.

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?

No guidance on when to invoke this versus review_requirements, decompose_optimization_query, or formulate_math_model. The pipeline position (presumably first, on a raw unformalized description) is implied by the wording 'raw user ... descriptions' but never stated as a condition.

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