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

Beyond the readOnly/idempotent/openWorld annotations, the description discloses concrete output behavior an agent cannot infer from structured fields: approval likelihood is always not_estimated, comparable_count and density range are always null, and comparability is not assessed. It also states the five-calendar-year decision window and that sources are national and freshness-unverified, which is exactly the kind of caveat an agent needs before relying on results.

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?

It is a dense single paragraph but uses explicit labels (Purpose, When to use, Returns, Limitations, Parameter guidance, Chaining) that front-load the most decision-relevant information. Every clause carries content, though the length sits near the upper bound of what is comfortable for a single block.

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, open-world tool with 6 parameters, nested site_ref chaining, and an output schema, the description covers purpose, exclusions, output caveats, parameter roles, and cross-tool chaining (site_ref reuse preserving coordinates, boundary confidence, LPA). Nothing an agent needs in order 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 parameter definitions already carry the baseline. The description still adds meaningful semantics: address and development_type are the ones to provide, proposed_units/site_area_ha only calculate supplied residential density, and proposed_height_m is recorded without any policy assessment — a non-obvious negative constraint not present in the schema.

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 names a specific verb (check) and resource (planning evidence readiness) scoped to a resolved England location, and it lists the concrete payload (bounded national-source sample, dated point-checked decisions from the last five years). It implicitly separates itself from siblings like get_planning_history or site_verdict, though it never names an alternative tool explicitly.

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?

It gives an explicit 'When to use' trigger (prepare proposal evidence, identify decision references to verify before policy and design appraisal) and an explicit exclusions block (no approval percentage, no three-approval threshold, no inferred scheme density, no local-adapter query). The Limitations section adds a clear when-not boundary by calling the output incomplete and freshness-unverified rather than a calibrated forecast.

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