UK Data: Country facts and words

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds two useful facts beyond them: the data source (Datamuse API) and a cost of 1 credit. It says nothing about result ordering, whether the source word is excluded, or how failures behave, so it is modest added value rather than rich disclosure.

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?

Three short sentences, front-loaded with what the tool returns. The 'Helps with vocabulary variety' sentence is slightly promotional filler but the credit-cost note earns its place. Nothing is buried or 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 simple two-parameter lookup with full schema coverage and read-only annotations, the description covers the essentials. But with no output schema, it could say more about what a result entry looks like or how 'closely related' differs from a plain thesaurus, leaving the agent to discover the shape of the response.

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 both 'word' and 'limit' are documented in the schema, which sets the baseline at 3. The description adds nothing about the parameters (e.g. whether limit defaults to 20 or the 1-100 range), and notably does not flag that the schema types 'limit' as a string despite describing it as an integer.

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: 'Returns a list of synonyms and closely related words for a given term.' The scope ('closely related words') is clear. It loses a point because it never distinguishes itself from the closely related sibling words_meaning_like, which by name covers nearly identical ground.

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 when-to-use or when-not-to-use guidance at all. With siblings like words_meaning_like, words_sounding_like, words_that_rhyme, and word_definition in the same family, an agent has to guess which one answers 'find me a similar word'.

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?

The annotations already declare that this is a read-only, idempotent, open-world lookup, so the description does not need to repeat those safety traits. It adds useful context by naming the data source (Free Dictionary API via Wiktionary), noting that multiple meanings and nuances are covered, and disclosing a cost of 1 credit.

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 short, front-loaded with the core action and return content, and uses the remaining space for source attribution and cost. Every sentence carries relevant 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 simple one-parameter lookup with annotations and no output schema, the description is largely complete: it says what content is returned and where it comes from. A minor gap is that it does not mention behavior when no definition is found.

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 single parameter is already well documented as the English word to define. The description reinforces that it is an English word but adds no new syntax, constraints, or interpretation 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 states a specific action and resource: looking up dictionary definitions, parts of speech, and usage examples for an English word. It is clear what the tool returns, but it does not explicitly differentiate itself from close siblings such as synonyms_for_word or word_pronunciation.

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 guidance on when to use this tool versus alternatives like synonyms_for_word, words_meaning_like, or word_pronunciation. The scope is implied by the phrase 'dictionary definition,' but no alternatives or exclusions are named.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds genuinely new context: the upstream source (Free Dictionary API via Wiktionary, CC BY-SA licensing) and a per-call cost of 1 credit, which matters for budgeting. It stops short of describing fallback behavior for unknown words or the shape/quantity of audio links.

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?

Three short sentences, front-loaded with the capability before the attribution and cost note. The attribution/licence line is slightly incidental but legitimately useful for provenance; 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?

With no output schema, the description carries the burden of describing returns and does so (IPA spelling plus audio links), and it discloses cost and data source. An agent can call this correctly; only edge-case behavior for words with no entry is unaddressed.

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% for the single 'word' parameter, so the baseline is 3. The description only reinforces that the word should be English, adding no format guidance (e.g. casing, phrases vs single tokens, handling of misspellings).

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 and resource ('Retrieve the phonetic IPA spelling and audio links for an English word') and even specifies what the payload contains. It does not explicitly distinguish itself from nearby siblings such as word_definition or words_sounding_like, so it falls short of the top mark.

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?

'Helps with correct pronunciation and spoken language' implies the use case but gives no explicit when-to-use or when-not-to-use guidance. It never points an agent to word_definition for meaning or words_sounding_like for homophones, leaving the routing decision to inference.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful context beyond that: the upstream data source (Datamuse API) and a cost signal ('cost: 1 credit'), which helps an agent budget calls. It does not say anything about result ordering or match quality, which keeps it below 5.

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?

Three tight sentences with the core purpose front-loaded, followed by the trigger condition and then the source/cost note. No filler or repetition of the title.

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 read-only lookup with rich annotations and full schema coverage, the definition supplies purpose, trigger, source, and cost — enough to invoke correctly. The only minor gap is that nothing characterizes the output (a ranked word list) or how the credit is charged, but with no output schema required this is largely covered.

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 both parameters (limit with range and default, description as required meaning string) are fully documented in the schema. The description adds no syntax or formatting guidance beyond the schema, 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 opens with a specific verb-and-resource framing: a 'reverse dictionary tool that finds words matching a description or concept.' That is clearly distinct from sibling tools such as word_definition (forward lookup), synonyms_for_word, words_that_rhyme, and words_sounding_like, and the follow-up sentence pins down the exact use case.

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?

'Ideal when you know the meaning but have forgotten the specific word' gives a concrete, recognizable trigger condition. It falls short of naming alternatives or when-not-to-use rules — nothing tells the agent to prefer word_definition or synonyms_for_word for adjacent needs — so it is clear context without explicit exclusions.

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so safety is covered. The description adds non-annotation context that actually matters: the Datamuse API is the upstream source and each call costs 1 credit. It does not describe the return shape or how many words come back, but that gap is minor for a read-only lookup.

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?

Four short sentences with the core purpose front-loaded and the credit cost placed at the end where it reads as a footnote. No wasted wording, though the API attribution and cost note could be condensed into one parenthetical.

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, read-only phonetic lookup with full schema coverage and no output schema, the description covers purpose, use case, upstream source and cost. Only the shape of the returned word list is unaddressed, which is a small gap given the tool's simplicity.

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 both parameters are already documented in the schema (including the 1-100 range and default of 10). The description adds nothing about the word format or how limit behaves, 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 gives a specific verb and resource ('Finds words that sound similar to a given word') and adds the distinguishing detail 'even if spelled incorrectly'. The phonetic-vs-semantic distinction from siblings like words_meaning_like or words_that_rhyme is implied by 'sound' and 'phonetic matching' but no sibling is ever named.

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 names two use cases ('spelling assistance and phonetic matching'), which tells the agent when this tool is appropriate. It gives no exclusions and never points to the obvious alternatives (words_that_rhyme, synonyms_for_word, words_meaning_like), so routing between the word-related siblings is left to inference.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the description's job is to add context - and it does, disclosing the upstream data source (Datamuse API) and a per-call cost of 1 credit, both of which matter for agent planning and budgeting. It stops short of describing result shape (whether words come back ranked/scored) or how the two parameters interact.

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?

Three short sentences with the core action front-loaded, followed by usage context, provenance, and cost. No filler, no restatement of the tool name, and the cost note is compactly placed at the end where it does not obstruct the primary definition.

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, read-only, two-parameter lookup with full schema coverage and no output schema, the description covers purpose, domain (English words), provenance, and cost adequately. The only real omission is any hint about what comes back - a bare word list versus ranked/scored matches - which would help an agent decide how to consume results.

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 and the description adds no parameter meaning at all - neither 'word' nor 'limit' is mentioned. Worth noting the schema itself is internally inconsistent (limit is typed as string while described as an integer 1-100, and no parameter is actually marked required despite 'word' being labelled required), and the description does nothing to compensate for that confusion.

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 opens with a specific verb and resource: 'Finds words that rhyme with a specific English word,' which is unambiguous and immediately actionable. It does not, however, differentiate itself from close siblings in the listing such as words_sounding_like, synonyms_for_word, or words_meaning_like, so an agent must infer the boundary itself.

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 sentence 'Useful for creative writing, poetry, and lyrics' implies a use context but never states when to choose this tool over its near-neighbours (e.g., words_sounding_like for near/slant rhymes, synonyms_for_word for meaning). There are no exclusions or alternatives, so routing depends on the agent's own reading of sibling names.

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