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

Annotations already cover read-only, idempotent, non-destructive behavior, so the description adds valuable context beyond that: accent-insensitive substring matching, the fact that matches are verified as actual substrings, and the return behavior (total_matches, total_is_exact flag when count is a lower bound, ordering by largest shelters, limit flooring and echoing). No contradictions 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?

The description is dense but every sentence earns its place: the core purpose is front-loaded, followed by matching semantics, a critical exclusion with alternatives, and return behavior. No filler or redundancy; it is as concise as needed for the information conveyed.

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 modest complexity (2 params, output schema present, annotations covering safety), the description covers everything needed for correct invocation: input type, matching rules, dataset limitations, routing to alternatives, and what the response contains (total_matches, total_is_exact, ordering, limit behavior). The presence of an output schema covers return details, so nothing essential 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 description coverage is 100%—both 'query' and 'limit' are well-described in the schema with examples and constraints. The description reinforces these but does not add significant new meaning beyond the schema; it merely restates the address nature of 'query' and the limit behavior already in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 states it 'Search(es) Swedish civil-defence shelters by ADDRESS text' with explicit examples of street names and property designations. It also explicitly distinguishes from siblings by noting it cannot list shelters in a town, which differentiates it from search_location and find_nearby_shelters.

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 gives explicit when-to-use guidance: it is for address-based lookups, and it explicitly says 'for shelters in <place> use search_location + find_nearby_shelters instead.' It also clarifies a dataset limitation (no municipality/city field) that prevents certain queries, leaving no ambiguity about alternatives.

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