record_outcome

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

With no annotations provided, the description fully discloses behavioral traits: it feeds the RL loop, the success flag is recorded as an agent_self_report event with strength=weak and include_in_default_training=False, and default labeling rules ignore it. This goes beyond basic functionality.

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 well-structured with a clear opening sentence, a paragraph explaining the RL purpose, an Args section, and a NOTE. Every sentence adds unique value, and there is no unnecessary repetition.

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 tool's complexity (2 parameters, no annotations, existing output schema), the description covers purpose, usage, behavioral details, and parameter semantics thoroughly. It distinguishes from siblings and provides actionable guidance.

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 description includes an explicit Args section that explains both parameters: fragment_ids (comma-separated IDs) and success (boolean, default true). This adds meaning beyond the input schema's type and requirement, especially given the 0% schema description coverage.

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's verb 'record' and resource 'outcome', explicitly stating it feeds the reinforcement learning loop. It distinguishes itself from sibling tools like record_test_result, record_command_exit, and record_ci_result, which are mentioned as alternatives for honest signals.

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 explicitly tells when to use this tool (for recording fragment outcomes for RL) and when not to (for honest training signals, use record_test_result etc.). It names specific alternatives and explains that default labeling rules ignore this tool's signal.

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