init_experiment

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

No annotations are provided, and the description does not disclose side effects such as whether existing files are overwritten, required directory state, or permission needs. The `force` parameter hints at destructive behavior but is not explained.

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 a single sentence that efficiently conveys the tool's purpose and optional elements. It is front-loaded and contains no redundant information, though slightly more structure could improve readability.

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 5 parameters and no output schema, the description covers the main action and output files but lacks details like behavior when directory exists, overwrite rules, or post-creation steps. This is adequate for a simple scaffold tool but incomplete for complex use.

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 60%, with descriptions for name, force, output, and with_pyproject. The description adds context about scaffolding files, but the `agent` parameter lacks description both in schema and description, limiting added value.

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 it scaffolds a CQ-runnable experiment, listing specific files (cq.yaml, train.py, optionally pyproject.toml and agent runtime assets). This specific verb+resource combination distinguishes it from siblings like run_experiment or validate_project.

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 implies use for initial setup, but provides no explicit when-to-use or when-not-to-use guidance compared to siblings. For example, it does not mention prerequisites or scenarios where validate_project might be more appropriate.

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