UK Data: UK places

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

With no annotations, the description carries the full load, and it does disclose something genuinely useful in the credit cost and the gov.uk data source. It does not state that this is a read-only lookup or what the results look like, so the safety profile and response shape remain implicit.

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?

Two tight fragments with the subject front-loaded and the cost caveat parenthesized at the end. Nothing is wasted, though the telegraphic style leaves the description thin rather than rich.

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 simple two-param lookup with no output schema and no annotations, the description is adequate but leaves real gaps. Both params are optional, yet there is no mention of what happens when year or division is omitted, and no hint of the returned fields.

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%, with 'year' documented as YYYY and 'division' listing the three valid values, so the schema does the heavy lifting. The phrase 'by division and year' only restates what the params already convey, adding no syntax or default information.

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?

States a specific resource and scope: UK bank holidays by division and year, sourced from gov.uk. The 'UK' qualifier implicitly separates it from the generic public_holidays sibling, but that differentiation is never made 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?

No guidance on when to use this versus public_holidays, working_days, or holiday_entitlement, which are the obvious adjacent tools. The agent must infer that this is the UK-specific path.

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?

With no annotations, the description usefully conveys a current-data lookup and discloses the one-credit cost. However, it leaves the data source, refresh cadence, and behavior for invalid postcodes unstated.

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 compact and front-loaded, covering purpose, scope, output content, and cost without filler. The parenthetical cost disclosure is efficient and easy to notice.

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 simple one-parameter lookup, it provides enough information to select the tool and generally invoke it correctly. With no output schema, the lack of intensity units, source or freshness details, and precise postcode syntax are noticeable but not fundamental gaps.

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?

Although schema coverage is reported as complete, the schema's "string" description adds little; the main description connects postcode to regional intensity and implies that national results are available without it. Accepted postcode granularity or format remains undocumented.

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 precisely identifies the resource, temporal scope, geography, and included generation mix: current GB grid carbon intensity at national or postcode-region level. This clearly distinguishes it from weather and other lookup siblings.

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?

"Current" and the national-versus-postcode-region distinction give clear selection context and implicitly exclude historical or forecast lookups. It does not explicitly say to omit postcode for national results or identify any alternative tool.

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?

With no annotations, the description carries the full burden and does add real context: the upstream source (data.police.uk), the spatial radius, the aggregation granularity, and the credit cost. It omits what happens when no crimes are recorded for the point/month, whether coverage is limited to England and Wales, and whether results are anonymised or snapped to approximate locations — meaningful gaps for a data-provenance tool.

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 dense sentence that front-loads the primary purpose and appends the source, output shape, and cost without wasted words. Every clause carries information an agent needs.

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 characterize return values, and it does so compactly ('counts by category and a sample'). Given a simple four-parameter lookup, this is close to sufficient; the missing pieces are location-pairing rules and no-data behavior rather than any structural gap.

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%, but the schema descriptions are placeholders ('number', 'string', 'YYYY-MM'), so the description's 'point or postcode' phrasing is what tells the agent that latitude/longitude and postcode are alternative ways to specify the location. It does not state that these are mutually exclusive or that one pair must be supplied, and all four parameters are marked optional with no stated defaults, so the pairing rules remain inferential.

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?

States a specific verb/resource (street-level crime), the spatial scope (within about a mile of a point or postcode), the temporal scope (for a month), and the shape of the result (counts by category plus a sample). This is precise enough to separate it from sibling police_neighbourhood, which covers a different granularity, though the description never names that sibling to make the distinction 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 'within about a mile of a point or postcode' clause implies when the tool fits, and the '(cost: 2 credits)' note signals it is a paid call worth using deliberately. There is no explicit when-to-use, when-not-to-use, or alternative routing (e.g., to police_neighbourhood or geocode) guidance.

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?

With no annotations, the description carries the full burden, and it does disclose two real traits: the data is 'live' (not cached/archival) and each call costs 1 credit. It says nothing about authentication, rate limits, result ordering, or what a warning record contains, which is a meaningful gap for an unannotated tool.

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 front-loaded sentence with the resource stated first and the cost annotation appended in parentheses; nothing is wasted. It is arguably too terse rather than bloated, but there is no filler to cut.

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 an unannotated, no-output-schema tool, the definition should describe the shape of a result (severity levels, affected areas, timestamps) and what limit does. Those are absent, leaving the agent to guess at return content and the meaning of the severity scale beyond the bare '1-4' in the schema.

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 nominally 100%, but the schema descriptions are placeholders ('number', 'string', 'number 1-4'), so the schema adds little meaning. The description clarifies that county is an optional geographic filter and that minSeverity is a 1-4 scale via the schema, but 'limit' is left entirely unexplained.

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?

States a specific resource (Environment Agency flood warnings and alerts) plus scope (optionally by county), which is far more informative than the bare name. It is implicitly distinct from siblings like weather or food_alerts, but never explicitly contrasts itself with weather, so it falls short of the sibling-differentiation bar for a 5.

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 when-to-use guidance and no mention of alternatives such as weather for general forecasts. The phrase 'optionally by county' hints at filtering but does not tell the agent when to supply a county versus omit it, or when this tool is preferred over a general weather lookup.

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?

With no annotations, the description carries the full burden, and it does disclose the billing cost of 1 credit plus the full output surface. It says nothing about invalid/malformed postcode handling, UK-only scope (only implied via LSOA/MSOA/ONS), or whether results are cached — real gaps for a lookup tool with zero annotation coverage.

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 compact, front-loaded line with the output list first and the cost tag last — nothing wasted. The verbless fragment style is terse but readable and does not obscure meaning.

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 single-parameter lookup with no output schema, the enumerated return fields substitute well for a missing output schema. Only the absence of error/scope behavior keeps it from being fully complete.

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?

Coverage is nominally 100%, but the schema text for the sole parameter is the placeholder 'string, required', so it conveys almost nothing. The description only reinforces that the argument is a postcode; it adds no format guidance (spacing, casing, partial postcodes).

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 the input resource (postcode) and enumerates the derived outputs (coordinates, region, local authority, ward, constituency, LSOA/MSOA, ONS codes), so the agent knows exactly what comes back. It stops short of distinguishing itself from the sibling 'geocode' tool, which plausibly overlaps on the coordinates portion.

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 explicit when-to-use statement, no exclusions, and no pointer to an alternative such as 'geocode' for address-level lookups or 'ons_search' for area statistics. Usage is only inferable from the tool name and the output list.

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