check_overadjustment

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

With no annotations, the description fully discloses behavior by explaining the causal logic behind each failure mode and the assumption that the DAG encodes the true structure. The note that DAG Studio verifies analyses but not the DAG's domain correctness adds important limitations.

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 bullet points, each explaining a distinct failure mode with citations. Every sentence contributes to understanding the tool's behavior or limitations, with no unnecessary filler.

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?

The tool has no output schema, so the description should logically hint at return values. It implies the output is the set of biased variables, but doesn't explicitly state the return format. However, the behavioral depth and input constraints make it largely complete for an agent to use 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by clarifying that dag and dagitty_string are alternatives and that adjustment_set is the target of evaluation, reinforcing the mutual exclusivity and purpose.

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 function: given a DAG and adjustment set, identify variables whose inclusion biases the estimate. It lists three specific failure modes (descendant_of_exposure, collider, descendant_of_collider), which distinguishes it from sibling tools like compute_bias or analyze_dag.

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 input requirements: either a canonical DAG object or dagitty_string, both paired with adjustment_set. It does not explicitly mention when not to use this tool or name alternative tools, but the context of evaluating adjustment sets is clear.

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