The short answer
Tool descriptions influence which operation an agent selects and what parameters it supplies. Descriptions should explain preconditions and consequences, not just the operation name.
The question and scope
This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai agent tool design in a documented setting. No measurements, participants or client outcomes are claimed here.
An illustrative test case
Two tools have similar names but different preconditions: one previews an action, the other commits it. The description experiment should measure confusion between intent and consequence.
This scenario defines a case to examine; it is not an observed result.
A proposed investigation
Hold tool implementation fixed while comparing terse, detailed and ambiguity-reduced descriptions. Replay tasks with overlapping tool names and invalid preconditions. Grade selection and parameter errors separately.
Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.
What to measure
- Wrong-tool selection.
- invalid parameters.
- recoveries.
- unnecessary calls.
Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.
Interpretation and limitations
Changing descriptions also changes context size. Record that difference and avoid concluding that verbosity itself is the cause.
Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.
A practical review checklist
- Is the question narrow enough for the available system or dataset?
- Are the comparison conditions genuinely comparable?
- Can a reader trace each conclusion to an observation?
- Are the boundaries and unresolved assumptions visible?
- Does the decision change if the result is negative or inconclusive?
The publication artifact
The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.
Sources and further reading
These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.