UK Data: Weather and places

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

With no annotations supplied, the description carries the full burden, and it does disclose two useful traits: the data source (OS Open Names) and the per-call cost of 1 credit. It omits behavioral details that matter here — how ambiguous names are resolved, whether multiple matches are returned, and geographic coverage limits — so the disclosure is partial.

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?

A single compact sentence that front-loads the input→output mapping, with the dataset and cost parentheticals trailing where they belong. No wasted words, though the compression is telegraphic enough that a little more structure would help.

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?

There is no output schema, so the description must convey return values — it does list coordinates, county, district, and region, which is the minimum needed. It does not explain the coordinate format, how results are ordered when 'count' > 1, or failure behavior for unknown names, leaving gaps for a 2-parameter lookup tool.

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%, so 'query' and 'count' (number 1-20) are already documented in the schema. The description adds no syntax, format, or matching-behavior detail for either parameter, so the baseline of 3 applies.

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?

Names a specific transformation (GB place name → coordinates, county, district, region) and cites the underlying dataset (OS Open Names), which is far more than a restatement of the name. It does not, however, differentiate itself from the obvious sibling postcode_lookup, which an agent could easily confuse it with for UK location resolution.

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?

There is no statement of when to use this versus postcode_lookup or wikidata_lookup, no note that it only covers Great Britain (so non-GB queries will fail), and no guidance on what to do when a place name is ambiguous. The agent must infer all selection logic.

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

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

No annotations are provided, so the description carries the full behavioral burden. It usefully names the upstream source (MET Norway) and a cost of 1 credit, which are real value-adds, but it says nothing about the 1-9 day cap, units, error behavior for an unrecognized postcode, or precedence when both place and coordinates are given.

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?

One front-loaded sentence covers the resource, the accepted input modes, the data source, and the cost. Nothing is padded and the most decision-relevant information comes first.

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 four-parameter tool with no required parameters, no annotations, and no output schema, the definition is adequate but leaves real gaps: what happens with zero arguments, what the default forecast length is, and how conflicting location inputs are resolved. An agent can probably call it successfully but may need a trial call to learn defaults.

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%, so the baseline is 3. The description does add meaning the schema lacks: that 'place' is UK-only while latitude/longitude work globally, which resolves a genuine ambiguity in a schema that just says 'number' and 'UK place or postcode'. It adds no detail on 'days' beyond what the schema already states.

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 gives a specific verb-and-resource pair ('Current conditions and daily forecast') plus a precise scope: worldwide coordinates, or a UK place/postcode. An agent immediately knows what it retrieves and the acceptable location inputs. It stops short of a 5 because there is no differentiation language for the two input modes (coordinates vs. place), which is the main ambiguity of the tool.

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?

Usage is implied by the stated coverage ('any coordinates worldwide, or a UK place or postcode'), so an agent can infer when the tool applies. However, there is no explicit guidance on choosing between the coordinate and place forms, no direction to geocode first when only a city name is known, and no stated default when no parameters are supplied.

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