UK Data: Reference

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

No annotations exist, so the description carries the full burden — it does add genuinely useful information in disclosing the cost ('1 credit'), which is not in any structured field. It omits other behavior an agent needs: whether pagination is supported, whether results are capped by default, and how conflicting parameters are resolved.

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 short sentences, front-loaded with the primary capability and no wasted wording; the cost note is a separate parenthetical that earns its place. Slightly under-specified rather than over-long, which is the right failure mode for this style.

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?

With no annotations and no output schema, the description is the only source of behavioral information, and it leaves the key ambiguity of a zero-required-parameter tool unresolved: precedence between isbn and query, and behavior with no arguments. For a simple lookup tool this is a meaningful gap rather than a minor one.

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 maps isbn to detail retrieval and query to title/author search, adding a little meaning, but it doesn't clarify whether query matches title and author simultaneously, nor does it explain the limit range (1-25) beyond what the schema already says.

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 verb and resource ('Book details', 'search books') and states the two supported access modes (ISBN lookup vs. title/author search). It does not reference any sibling tool, but none of the listed siblings (wikipedia_summary, arxiv_search, papers_search) overlap directly, so differentiation is less critical here.

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 two modes imply when to use each parameter (ISBN for a known book, query for discovery), but this is inference rather than guidance. It never states what happens if both isbn and query are supplied, or what occurs when neither is, which matters because the schema declares zero 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 supplied, the explicit "(cost: 1 credit)" disclosure is valuable behavioral context beyond the schema. However, the description does not address precision or rounding, handling of invalid or incompatible units, or how the converted result is represented.

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 front-loads the operation and supported scope, then states the cost in a compact parenthetical. The category list is substantive rather than filler, and there is no unnecessary wording.

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 three-parameter conversion utility, the scope and cost are sufficient for basic tool selection. Given the generic string parameters and absence of unit enums, annotations, or an output schema, concrete invocation examples and brief error, precision, and result-format details would meaningfully reduce uncertainty.

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%, establishing a baseline of 3, although those descriptions are minimal. The unit-category list adds some meaning to from and to, but the description provides no accepted unit names or symbol examples and does not explain string values or incompatible unit pairs.

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 verb "Convert" and the enumerated categories clearly state the operation and its scope, including the useful distinction between UK and US volume units. It does not explicitly differentiate itself from related siblings such as convert_time, calculate, or fx_rates.

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 supported-category list provides implicit guidance about when this tool is appropriate. There is no when-not guidance, alternative-tool routing, or clarification of boundary cases such as currency and time-related conversions.

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 discloses the 1-credit cost and the formatting modes, but it does not describe error behavior, whether currency conversion is performed, or what the output looks like.

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 short cost note. Every element is relevant and there is no unnecessary wording.

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 and the schema covers all parameters, but with no output schema the description should ideally state what formatting result is returned. It also mentions compact form even though no such parameter exists in the schema, which leaves a small completeness 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 description coverage is 100%, so the schema already documents amount, locale, currency, and words. The description adds a use case for words (cheques and contracts), but it also mentions a compact form that is not represented in the schema, leaving some ambiguity without adding deeper meaning to the documented 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 states a specific verb and resource: formatting a money amount with currency, locale, compact form, and optional words. It is clear what the tool does, but it does not distinguish itself from nearby siblings such as calculate, convert_units, or fx_rates by explaining why an agent should choose this formatter over those tools.

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 an implied use case for the words option, noting it is for cheques and contracts, and mentions compact form. However, it does not state when to use this tool rather than currency-conversion or calculation siblings, nor does it give explicit exclusions or 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 supplied, the description carries the full burden, and it does add two genuinely non-obvious facts: the data source (RDAP) and a cost of 1 credit. It says nothing about behavior for malformed or private/reserved IPs, IPv4 vs IPv6 support, rate limits, or failure modes, so the mutation/safety profile is only partially covered.

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 clause naming the question and the returned fields, followed by a compact parenthetical for cost. Nothing is wasted 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 single-parameter lookup with no output schema and no annotations, the description does well to enumerate the return fields, which substitutes for the missing output schema. However it leaves open input format and error behavior, which matters for an agent that must decide whether an arbitrary string is a valid argument.

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 nominally 100%, but the only parameter is documented as 'string, required', which conveys no real meaning, so the description must carry the load. It does establish that the parameter is an IP address, but adds no format expectations (dotted-quad vs CIDR vs IPv6) beyond that, so the baseline 3 applies rather than more.

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 (an IP address) and the question answered (ownership), plus the concrete fields returned (network name, range, country, organisations). It is clear what the tool does, but it does not differentiate itself from the sibling rdap_lookup, which the parenthetical '(RDAP)' actually makes look redundant.

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 or when-not-to-use guidance, and no pointer to alternatives such as rdap_lookup, domain_check, or ssl_check, which an agent could easily confuse with this tool. The agent has to infer applicability purely from the noun phrase 'IP address'.

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 burden, and it does disclose the credit cost and the shape of returned data, including that nutrition is normalised per 100g. It omits what happens for an unknown barcode (error vs empty result), geographic/product coverage, and whether results are cached or rate limited.

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 sentence front-loads the resource and key, then lists return fields and the cost in parentheses. No filler, no repetition of the tool name.

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 with no output schema, the enumeration of return fields plus the per-100g basis and credit cost is nearly sufficient. The remaining gap is edge-case behaviour on invalid or unknown barcodes, which an agent would need to handle gracefully.

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 the single 'barcode' parameter is formally documented, and the description adds the meaning that it identifies a food product. It does not specify accepted barcode formats (EAN-13, UPC-A) or whether the check digit is required, which is the one detail an agent could actually get wrong here.

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 resource (a food product) and the lookup key (barcode), then enumerates the returned fields (name, brand, ingredients, allergens, Nutri-Score, nutrition per 100g). That is specific enough to separate it from siblings like product_recalls or food_alerts, though the verb ('look up') is only implied.

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 barcode key, and the '(cost: 1 credit)' note gives a real decision input for an agent weighing calls. However, it never states when to prefer this over related tools such as product_recalls or food_alerts, nor any precondition (e.g., needing an EAN/UPC code in hand).

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, and it does disclose the credit cost and the shape of the returned data, which is real value. It says nothing about behavior on a missing/ambiguous title, language fallback, or truncation of the lead summary, which are the main failure modes an agent would want to know about.

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 with zero filler, front-loading the return contents and appending the cost note where it is easy to scan. Slightly weakened by leading with output fields rather than the action itself.

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 lookup tool this covers the essentials, and listing the returned fields compensates for the absent output schema. It remains thin on lookup failure behavior and on selection versus sibling tools, which is what an agent would most need beyond 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 100% and both parameters are documented in-schema (two-letter lang code defaulting to 'en', title required). The description adds no additional meaning about title format such as page title vs. free text or disambiguation handling, so the baseline 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?

The description names a specific resource (a Wikipedia article) and enumerates exactly what it returns: lead summary, description, coordinates and Wikidata id. However, the verb is implicit (no 'get'/'fetch'), and it does not explicitly distinguish itself from the nearby wikidata_lookup sibling, which overlaps on the Wikidata id field.

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 guidance on when to use this versus wikidata_lookup, news_search, or read_url for encyclopedic facts. The only usage-adjacent information is the cost note, which is helpful but is not a when-to-use/exclusion rule.

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