check_identification

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

Annotations already mark it readOnlyHint=true, and the description adds behavioral context: it is a pre-fit design check, not an estimator, and it surfaces design problems. It does not disclose error behavior or output structure, but the output schema exists and the read-only semantics are already captured.

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?

Two sentences, no filler, with the most important guidance front-loaded: what it does, what it checks, and when to run it. Every word earns its place.

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 output schema and the descriptive parameter comments for data_path, detail, strict, as_handle, data_columns, data_sample_n, and result_id, the description is largely complete for a pre-estimation diagnostic tool. The main gap is that it does not clarify which design-specific parameters are needed for each check, but the overall usage context is well established.

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 only 44%, with many core causal parameters (y, id, time, cohort, design, treatment, covariates, instrument, running_var) undocumented. The description's checklist ('bad controls, overlap, cohort sizes, IV first-stage F, clustering') provides conceptual hints that map loosely to these parameters, but it does not explain individual parameter roles clearly enough to fully compensate for the low 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 names a specific action ('design-level identification diagnostics') and concrete check dimensions: bad controls, overlap, cohort sizes, IV first-stage F, clustering. It is clear what the tool does and ties to the tool name, but it does not explicitly contrast with sibling diagnostic tools like iv_diag or balance_diagnostics, so it misses the top mark.

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 line 'Run BEFORE fitting any estimator to surface design problems' gives an explicit temporal placement and intended use. It does not state when not to use it or name alternatives, but the placement guidance is strong enough for an agent to know when to invoke it.

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