influence_functions

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

The description explicitly states the tool can 'optionally written to disk', a write operation, while the annotations declare readOnlyHint=true. This is a direct contradiction. Although the description adds rich behavioral context (validation tier, failure modes, typical N), the contradiction with the read-only hint is a serious issue that warrants a score of 1.

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 long but well-structured, with sections for purpose, validation, pre-conditions, failure modes, alternatives, and typical N. It front-loads the core action and flows logically. While it's not terse, every sentence contributes useful operational context, making it appropriately concise for a tool with multiple failure modes and pre-conditions.

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 tool's complexity (8 parameters, 1 required, pre-conditions, failure modes, alternatives) and the absence of a visible output schema, the description covers all essential aspects: what it does, when to use it, what prerequisites are needed, what can go wrong, and what to use instead. The typical minimum N adds practical guidance. Nothing an agent needs to call it correctly is missing.

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 all parameters are already documented in the input schema. The description adds minimal parameter-specific meaning beyond the schema—it mentions the purpose of the export and the path for writing, but doesn't elaborate on parameter semantics beyond what the schema provides. This matches the baseline for high schema 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 states a specific verb ('Export'), a precise resource ('per-unit influence functions of a Callaway-Sant'Anna fit'), and the output form ('tidy, self-contained DataFrame'). It also distinguishes itself from siblings by naming alternatives (sp.aggte, sp.aggte_from_influence), making it unmistakable what this tool does and what it is not.

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?

Explicit when-to-use guidance: 'Feed the export to sp.aggte_from_influence for post-hoc custom aggregation without refitting or re-loading the data.' It also lists alternatives and gives pre-conditions (result produced by sp.callaway_santanna) and failure modes (if result lacks influence functions, fit with sp.callaway_santanna first). The agent is told exactly when to choose this over siblings.

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