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workbench_record_execution

Records model response text as evidence artifacts for AI coding-agent runs, enabling validation and quality gates.

Instructions

Capture Goose/model response text into Workbench evidence artifacts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectYes
run_dirYes
response_textYes
files_touchedNo
model_output_statusNoresponse_captured
run_statusNoin_progress
response_sourceNogoose
validationNo
follow_upNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations, the description must detail behavioral traits. It only mentions 'capture,' implying a write operation, but fails to disclose if data is appended/overwritten, required permissions, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise, but it lacks structure and key details. It is front-loaded but too sparse to be effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 9 parameters and no annotations, the description is far too brief. It provides only a high-level purpose without covering critical context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds no information about any of the 9 parameters. It does not explain what 'project,' 'run_dir,' or 'response_text' mean, leaving the agent without guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool captures 'Goose/model response text into Workbench evidence artifacts,' using a specific verb and resource. It distinguishes from sibling tools like workbench_analyze_runs, though it could be more explicit about its unique role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use or avoid this tool, or how it differs from siblings like workbench_validate_run. A single sentence does not provide usage context.

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

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