suggest_variables_for_research_question

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

Annotations already provide readOnlyHint=true and openWorldHint=true. The description adds value by mentioning 'cautions', suggesting the tool also provides warnings or limitations, which extends behavioral context beyond structured 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?

Single sentence that efficiently communicates action, input, and output. Every word earns its place with no 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?

Has an output schema, which reduces the burden on description for return values. Description covers the core purpose and adds 'cautions'. Could elaborate on what cautions entail, but sufficient given output schema exists.

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 coverage is 0% for the single parameter 'question', so the description must compensate. It clarifies the parameter is a 'natural-language research question', but provides no format guidance or examples. Adequate but not enhanced.

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 uses a specific verb 'Map' and specifies the resource 'natural-language research question' and output 'likely GHED variables and cautions'. It clearly distinguishes from siblings like search_variables that perform string matching rather than conceptual mapping.

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?

Implies usage for research questions but does not explicitly state when to use this tool versus alternatives like search_variables or list_variables. No when-not or exclusion criteria provided.

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