from_stata

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

Despite annotations already marking it read-only, the description adds useful behavioral details: it returns a verified payload, never guesses on unrecognised commands, and instead returns close-match suggestions. It also discloses the dual output format (python_code and arguments), which helps the agent understand what the tool actually produces. This goes beyond the minimal annotation signal without contradicting it.

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 compact and front-loaded: the first sentence states purpose and output, the second scopes supported commands, and the third explains fallback behavior. Every sentence earns its place, and there is no redundant restating of schema details or annotations.

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 rich input schema and existing output schema, the description is sufficiently complete for an agent to know what commands are supported, what the tool returns, and what happens for unrecognised input. It lacks an explicit contrast with sibling tools like from_r, but that gap is more about usage guidance than invocation completeness. Overall, the agent has enough context to call the tool correctly in most cases.

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 input schema already documents all seven parameters thoroughly. The description adds modest context by listing supported command forms and noting that multi-command lines must be split, but the schema’s command example already conveys this. With full schema coverage, the description does not need to compensate heavily, so the baseline 3 applies.

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 first sentence states a specific verb and resource: translating a single Stata command into a verified StatsPAI tool-call payload, and even distinguishes the output format (python_code and arguments). The Stata focus clearly separates it from sibling tools like from_r and other estimation tools. The list of supported command families further sharpens the purpose.

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 gives implied usage context: it is for Stata commands, especially Tier-1 commands like regress, reghdfe, and rdrobust, and for count-panel commands like nbreg. However, it does not explicitly state when to use this tool instead of from_r or another translation/estimation sibling, nor does it offer exclusions such as 'use from_r for R code'. The supported-command list implies scope but does not fully spell out the alternatives.

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