honest_did_from_result

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

Annotations already declare readOnlyHint=true, so the description does not need to cover safety. It adds value by disclosing that betas, sigma, and counts are auto-extracted from the result, and that the LLM does not need to pass arrays – a concrete behavioral detail. It also names the method (Rambachan-Roth 2023). No contradiction with annotations.

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, both purposeful. The first front-loads the core purpose and the second explains a key convenience (auto-extraction) that shapes how the LLM should call the tool. No wasted words.

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 read-only tool with an output schema, the description covers the essential usage: it operates on a fitted result identified by result_id and auto-extracts necessary components. It does not explicitly state it requires a DID/event-study result type, but that is implied by the name and first sentence. Given the rich schema and output schema, it is adequately 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 descriptions cover 100% of parameters, each with a detailed explanation (e.g., detail levels, method options, data_path formats). The description's note about auto-extracting betas/sigma/counts reinforces the role of result_id but does not add significant meaning beyond the schema. 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 description states a specific action (computes honest CIs) on a specific resource (fitted DID/event-study result), and distinguishes itself from siblings like honest_did by emphasizing it works on a fitted result and auto-extracts arrays, so the LLM never ferries arrays. This clearly differentiates it from tools that take raw data.

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 clearly implies it should be used when a fitted DID/event-study result already exists, and that the user should pass a result_id rather than raw data. However, it does not explicitly name alternatives or state when not to use it, leaving some inference to the agent.

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