get_door_effect_summary

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

With no annotations, the description carries the full transparency burden. It discloses the historical nature, exact denominator, data source, attribution, and non-predictive/non-recommendation status. It does not specify the exact return format or table schemas, but provides substantial behavioral context beyond the name.

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 dense but front-loaded with the main finding and key statistic. The methodology, caveats, and attribution add length but each sentence provides distinct useful context; it is not padded or repetitive.

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 zero-parameter summary tool with no output schema, the description covers the data source, scope, computation details, and limitations. It falls slightly short by not enumerating the exact response fields or table structures, but the provided context is largely sufficient for an agent to understand what will be returned.

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?

The tool has zero parameters, so the schema fully covers parameter semantics. The description appropriately adds context about the fixed dataset and methodology instead of parameter details, matching the baseline for parameterless tools.

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 identifies the tool's output as a variance decomposition for 'The Door Effect' with specific statistics and lender comparison tables, making the purpose concrete. However, it never explicitly states an action like 'returns' or 'provides', relying on the tool name to convey the verb.

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 explains what the data is and includes caveats about prediction and recommendations, but it gives no guidance on when to use this tool versus siblings such as get_national_fha_stats or get_lender_denial_stats. No alternatives or exclusion criteria are mentioned.

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