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

Annotations already declare readOnly/idempotent/closed-world, yet the description goes well beyond them: it discloses that the figure is associational not causal, that HMDA carries no credit scores, the exact data provenance (CFPB HMDA 2025, actions 1,2,3, loan_type 2), the sample size, and the McFadden range. That is rich behavioral context an agent could not get from structured fields.

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 'ANSWERS:' and 'NOT FOR:' labels front-load the key information and each clause carries signal (headline statistic, caveat, provenance, license). It is dense but slightly overpacked, with the attribution and dataset-provenance tail being useful but not essential to invocation.

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?

With no parameters, no output schema and safety already covered by annotations, the description fully compensates by describing the return value (the variance decomposition and headline 38%/859,090/McFadden figures), its interpretation limits, and data source. An agent has everything needed to select and trust the result.

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?

There are zero input parameters, so the schema cannot be under-documented; per the rubric this sets a baseline of 4. The description adds no parameter syntax because none exists, which is appropriate rather than deficient.

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 and resource ('Returns the variance decomposition: lender identity is associated with about 38 percent of the explainable variation in FHA denial outcomes'), which is concrete and quantitative. It implicitly distinguishes itself from siblings like get_lender_denial_stats and get_conditional_door_map by framing the output as a single aggregate decomposition, but it never names an alternative tool, so sibling differentiation is inferential rather than explicit.

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 'ANSWERS:' framing gives clear positive usage contexts and the 'NOT FOR:' clause explicitly excludes causal claims and individual estimates, which is strong when-not guidance. However, it never routes the agent to the sibling tool to use instead (e.g., for per-lender or individual-level questions), leaving the alternative choice to inference.

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