save_golden_set

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool evaluates measures, writes a deterministic, git-friendly JSON file sorted by name with no timestamps, and records error-state goldens. This is substantial transparency about side effects and deterministic behavior, though it does not mention overwrite behavior or the need for an open model session.

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?

The description is two sentences that are information-dense but front-loaded with the core purpose. Every phrase earns its place: the mechanism (EVALUATE ROW), output properties (sorted, no timestamps, git-friendly), error handling, and replay pointer. 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?

Given the absence of annotations and output schema, the description is quite complete. It covers purpose, behavior, error recording, and a companion tool. The only notable gaps are the role of sessionId and whether the file is overwritten if it already exists, which would be important for an agent using the tool correctly.

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?

The schema already describes path and measures, and the description adds the meaning of 'comma-separated subset' for measures, which is helpful. However, the required sessionId parameter is not explained in either the schema or the description, leaving ambiguity about its purpose. With 67% schema coverage, the description only partially compensates for this gap.

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 clearly states the tool captures a measure-regression baseline, evaluates model measures via EVALUATE ROW, and writes a golden-set JSON file. It uses specific verbs ('capture', 'evaluate', 'write') and distinguishes from the sibling run_golden_set by explicitly mentioning replay with that tool.

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?

The description provides clear context for when to use this tool: to establish a baseline for measure regressions. It also references run_golden_set for replay, implying a workflow. However, it does not explicitly state when not to use it or compare it to other snapshot tools like sentinel_snapshot, so it lacks explicit exclusions.

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