get_labtalk_variable

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

With no annotations, description carries full burden. It discloses that missing numeric variables return 0 and typed variables are script-local. It also states return type. However, it doesn't cover behavior for missing string variables or error cases.

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?

Extremely concise: one-line purpose, gotchas presented clearly, then args/returns. Every sentence adds value, no wasted words.

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?

For a simple tool with one parameter and output schema, description covers purpose, parameter semantics, return type, and behavioral gotchas. Minor gaps: missing behavior for non-existent string variables. Still adequate.

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?

Despite 0% schema coverage, the description adds critical details: the naming convention ($ suffix for strings) and example ('str$'). This clarifies parameter usage far beyond the raw schema.

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 first sentence clearly states the tool retrieves the value of a LabTalk variable. It differentiates from siblings like run_labtalk which executes scripts, and set_* tools which modify variables.

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?

Gotchas provide guidance on when to use this tool: it notes that numeric non-existent variables read as 0 and typed variables vanish, advising to use untyped assignment in run_labtalk for persistence. While not explicitly stating when-not, it implies using run_labtalk for setting persistent variables.

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