sentinel_hte

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

With no annotations, the description carries full burden. It discloses the method (causal forest) and output (effect size, confidence interval, honest caveats), but omits any side effects, permissions, or rate limits. Adequate for a read-only query, but not detailed.

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?

Single sentence front-loading the method and purpose, then detailing parameters and output. No wasted words, highly efficient.

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 no output schema, the description explains the return values (effect size, confidence interval, caveats). It covers the essential behavior for a simple causal inference tool, though it could mention return format or error handling.

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 describes parameters minimally (ISO code, treatment label). Description adds concrete examples (election, protest, internet-law) and links treatment to censorship outcomes, providing meaning beyond schema. With 100% coverage, it adds value.

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 estimates heterogeneous treatment effects via causal forest, requiring a country and treatment, returning effect size with confidence interval. It distinguishes itself from sibling tools by its specific causal inference focus on censorship outcomes.

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?

Provides examples of treatments and context for when to use (for causal effect estimation), but does not explicitly exclude alternatives or compare with sibling tools like sentinel_outcomes or forecast tools. Implied usage, no when-not guidance.

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