evaluate_co_scientist_run

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful state information ('completed or in-progress') and the kinds of metrics measured, but it does not state whether evaluation is read-only, whether it persists anything, or what it returns. This leaves important behavioral traits unstated.

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 organized with a one-line purpose, a scannable trigger list, and a parameter list. It is longer than strictly necessary because the parameter section duplicates schema information, but that duplication is justified by the need to add semantics.

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?

For a five-parameter tool with no output schema and no annotations, the description covers triggers and parameter meanings but omits return value/format, side effects, and prerequisites such as whether the run must already exist. The overlap with compare_co_scientist_workflows also leaves a selection gap. It is adequate but not complete.

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 description coverage is 0%, but the description compensates fully with a PARAMETERS section that explains each field: run_id as the run identifier, token_count/cost_usd as optional resource usage, human_edits_needed as report edit count, and expert_accepted as a user/expert acceptance flag. This gives clear meaning beyond the bare schema titles.

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 opening sentence names a clear verb and resource ('Evaluate a completed or in-progress Co-Scientist run') and the trigger bullets specify measurable goals such as traceability, citation coverage, and cost. However, the third trigger, 'Comparing the two-session workflow against simpler baselines,' overlaps with the sibling compare_co_scientist_workflows, so the description does not fully distinguish itself from that alternative.

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 provides 'AUTOMATIC TRIGGERS - Call this when' with three concrete scenarios, giving clear context for invocation. It does not state when not to use the tool or point to an alternative for comparison, so it misses exclusions.

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