UK Data: UK trade, property and planning

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

No annotations are supplied, so the description carries the burden of disclosure. It adds useful cost information and indicates the response includes current ratings, but it does not state read-only behavior, error handling, or auth/rate-limit constraints; for a simple lookup this is adequate but thin.

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 a single front-loaded sentence plus a cost note, with no filler. It could be slightly more structured, but every element earns its place.

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 one-parameter lookup, the description covers the resource, identifier, main return content, and cost. It leaves out how to obtain a location id and how this differs from cqc_search, so it is adequate rather than 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?

The single locationId parameter is fully covered by the schema, so the baseline is 3. The description reinforces that the parameter is a CQC location id but adds no format, validation, or example beyond 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 resource (CQC location), scope (by location id), and return content (details and current ratings). It is clear enough to distinguish a by-id lookup from a generic search, though it does not explicitly name cqc_search as the alternative.

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 or when-not-to-use guidance. 'By location id' implies a direct lookup when an ID is known, but it does not tell the agent to use cqc_search first or explain prerequisites.

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 at all, the description carries the full burden, and it usefully discloses the cost model ("cost: 1 credit"), which is real behavioral information not available elsewhere. However, it says nothing about matching semantics (partial vs. exact, postcode length/format), default result count, or the shape of returned results, which matters 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?

One sentence plus a parenthetical cost note; the resource and lookup keys are front-loaded and there is no filler. Nothing in it could be removed without losing information.

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 search tool with no output schema and no annotations, the description should at least hint at what comes back (e.g., location IDs usable with cqc_location) and how limit affects credit cost. It covers the search intent adequately but leaves the agent guessing about result contents and follow-up calls.

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 baseline is 3, but the schema's parameter descriptions are only type placeholders ("string", "number"), meaning the description is doing most of the real work by labeling name and postcode as the search keys. It adds nothing about the limit parameter (default, max, or interaction with credits), leaving the most ambiguous parameter 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?

The description names a specific verb ("Find") and a concrete resource set ("CQC-registered care homes, GP practices, hospitals and clinics") plus the lookup keys (name or postcode), so an agent immediately knows what the tool returns. It does not distinguish itself from the sibling cqc_location, which likely retrieves details for a single provider, leaving that routing inference to the agent.

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?

"by name or postcode" implicitly tells the agent this is the entry point when searching for a provider, but there is no explicit when-to-use vs. the cqc_location sibling, no stated prerequisites, and no guidance for what to do when neither key is supplied (0 required parameters).

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 at all, the description carries the full disclosure burden, and it does add real value by stating the data source (HMRC) and the cost ('1 credit'), which is a behavioral trait an agent otherwise could not know. It does not say what happens if the optional month is omitted (assume latest month?), how far back history goes, or what the rate payload looks like. Useful, but incomplete 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 tightly written clause covering resource, source, scope, and cost, with no filler. The credit cost is appended in parentheses rather than front-loaded, but everything present earns its place.

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?

The tool is simple (one optional string parameter, no output schema), so the bar is low, and the description covers source, scope, and cost. The main omission is default behavior when month is absent, which is exactly the ambiguity an agent faces with an optional-only parameter set and no annotations to fall back on.

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% and the single parameter 'month' is documented as 'YYYY-MM', so the schema already carries the semantics. The description's word 'monthly' reinforces the granularity but adds no format, defaulting, or range information beyond the schema. Baseline 3 is appropriate.

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 resource with source and scope: 'HMRC monthly exchange rates for customs and VAT, every currency to GBP.' An agent immediately knows what data comes back (monthly HMRC rates quoted against GBP). It stops short of naming the sibling it differs from (fx_rates, boe_series), so the distinction is inferable from the 'HMRC/customs/VAT' qualifier rather than stated.

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?

'for customs and VAT' hints at the use case, implying this is the authoritative rate set for HMRC filings rather than a generic market rate feed. However, it never states when to prefer this over fx_rates or boe_series, and gives no prerequisites or exclusions. Usage is implied, not guided.

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 useful traits: the 1-credit cost and the shape of the returned data (price, index, annual change). It says nothing about whether the call is read-only (inferable from the resource), error behaviour for an unrecognised slug, or how the 'latest' month is determined.

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 tightly packed sentence with the resource, measures, granularity and scope front-loaded, followed by slug examples and the cost. No filler, nothing repeated from the schema.

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-parameter read tool with no output schema, the description is close to sufficient: it states what comes back, what geography means, and the cost. The remaining gap is the semantics of 'months' (window size, ordering, default when omitted).

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 text is effectively empty ('string', 'number 1-120'), so the description does real work by explaining that 'area' is a slug and giving three concrete examples at different geographic levels. It leaves 'months' unexplained beyond the schema's 1-120 range, so it does not fully compensate for both parameters.

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 specific dataset (UK HPI) and the three returned measures (average price, index, annual change), with the granularity (by month) and the geographic scope (country, region or local authority). This separates it from siblings such as price_paid or ons_timeseries, though it never explicitly contrasts itself with them.

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 geographic scope and slug examples, making it obvious this is the tool for UK house price statistics. There is no explicit when-to-use guidance, no statement of when to prefer price_paid or ons_timeseries, and no mention of data lag or coverage limits.

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 behavioral burden. It usefully adds that results come via PlanIt and that a call costs 2 credits, but it does not state read-only behavior, pagination, rate limits, or what the response contains.

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 a single front-loaded sentence with no wasted wording. The cost parenthetical is compact and adds practical information without disrupting scanability.

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 low-complexity search tool with full parameter coverage, the description is minimally adequate. However, it does not explain result behavior, how search criteria combine, or whether at least one criterion is required despite all parameters being optional.

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 structured schema is the baseline source of parameter meaning. The description maps keyword, postcode, and authority to parameters but adds no guidance on limit, daysBack, or radiusKm beyond what the schema already provides.

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 uses a specific verb, 'Search', a precise resource, 'planning applications', and a clear scope, 'across UK councils'. It also names the searchable criteria, so an agent can distinguish this from general search or legislation tools without opening the schema.

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 indicates that keyword, postcode, or authority can be used as search criteria, which implies usage. However, it gives no explicit when-to-use guidance, no exclusions, and no comparison to sibling search tools.

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 burden. It does add one genuinely useful behavioral fact — a cost of 2 credits — but omits any statement about auth requirements, caching, failure modes, or the shape of the returned duty/measure data.

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 two parallel clauses plus a parenthetical cost note. Every element earns its place and nothing is padded.

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 two-parameter lookup tool with no output schema and no annotations, the description covers the inputs and sketches the returns (duty rates, import measures) adequately. The only real gap is what the measure data actually contains, which an agent must discover by calling.

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% and already explains the 4-digit vs 10-digit distinction, so the baseline is 3. The description does reinforce the role of each parameter (description search vs code lookup), but adds no format examples or precedence rule beyond what the schema 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 names a specific verb+resource pair ('Find a UK commodity code') and a second concrete mode ('get duty rates and import measures'), so the agent knows exactly what the tool returns. No sibling in the list touches tariffs or trade data, so disambiguation is implicit but unambiguous.

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 clearly splits the two usage modes: pass a description to look up a code, pass a code to get duty rates and measures. It gives no exclusions and does not say what happens if both parameters are supplied or if neither is supplied, which matters since zero parameters are required.

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