eval_ingest_trace

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

No annotations are provided, so the description carries the full burden. It discloses supported frameworks, expected input structure (trace_json must include input and steps), and the return shape (keys like input, expected_output, agent_trace). It does not cover error handling or edge cases, but for a conversion tool, the disclosure is adequate.

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 summary, usage guidance, framework details, and structured Args/Returns sections. It is informative without being overly verbose, though it could be slightly shorter while retaining clarity.

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 (multiple frameworks, conversion task) and minimal schema, the description is complete. It explains the output shape (return dict keys) so that the agent knows how to use the result with other eval_* tools. The presence of an output schema in signals does not detract from the description's completeness.

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 has 0% description coverage, so the description must compensate. It does so thoroughly: explains that trace_json must include 'input' and 'steps' (or 'new_items' for openai_agents), and lists the three valid framework values. This adds significant meaning beyond the raw schema.

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 states exactly what the tool does: 'Convert a JSON agent trace into a JSON-friendly EvalCase payload.' It is specific about the verb ('Convert') and the resource ('agent trace into EvalCase payload'), and it clearly distinguishes itself from sibling eval_* tools by focusing on trace ingestion rather than evaluation.

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 says when to use: 'Use this when your agent has just finished a trajectory at runtime and you want to score that trajectory immediately.' It provides context for usage but does not explicitly state when not to use it or compare to alternatives. The guidance is clear but could be more comprehensive with exclusions.

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