UK Due Diligence

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

Annotations already declare readOnly, non-destructive, and idempotent. The description adds meaningful behavioral context about what data to expect (trustees, income/expenditure, insolvency flags) and that it returns a 'full' profile. This goes beyond the annotations without contradicting them.

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 sentences, front-loaded with the core action, and no filler. The second sentence efficiently lists return contents. Every word 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?

Given the simple one-parameter schema and availability of an output schema, the description covers the essential context: what it fetches, what it returns, and the prerequisite step. No significant gaps remain.

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 parameter description already explains the format and origin ('Returned by charity_search'). The tool description reinforces the usage but doesn't add new meaning beyond the schema beyond implying the parameter is central to the lookup.

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 the specific verb 'Fetch' with a clear resource ('full Charity Commission profile') and the input parameter ('charity number'). It lists concrete data fields returned (trustees, income/expenditure, insolvency flags, etc.), which distinguishes this from sibling tools like charity_search or company_profile.

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?

Explicitly instructs to use charity_search first to obtain the charity number, which is a clear prerequisite and aligns with the sibling tool. This tells the agent when to use this tool versus the search alternative.

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, and destructiveHint=false. The description adds valuable context: the upstream endpoint returns the full list at once, and pagination is client-side, plus it lists the return fields (registration number, status, registration date). This goes beyond the annotations without contradicting them.

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 sentences with no waste. The purpose is front-loaded, the return values and alternative tool are summarized, and the pagination nuance is explained. Every sentence 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?

Given the tool's simplicity, the output schema, and strong annotations, the description covers all essential aspects: purpose, return fields, alternative usage, and pagination behavior. No significant gaps remain.

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 baseline is 3. The description adds meaning by clarifying that offset/limit are client-side slicing over a full upstream list, which informs how to use these parameters effectively. This is more than the schema alone provides, though it doesn't detail each parameter syntactically.

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: 'Search the Charity Commission register of England and Wales by name or keyword.' It clearly distinguishes this from the sibling charity_profile by stating 'Use charity_profile for full details once you have the charity number.'

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 explicitly tells when to use this tool (for name/keyword search) and when to use the alternative (charity_profile for full details after obtaining the number). It also describes the pagination workflow via offset/limit, giving clear context for usage.

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 readOnly, idempotent, and non-destructive behavior. The description goes further by disclosing that resigned officers are excluded, pagination is handled internally, and ignored parameters are accepted only to avoid call failures.

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?

Every sentence earns its place: purpose, return content, exclusion of resigned officers, and pagination guidance. The description is concise, well-structured, and front-loaded with the core action.

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 tool with an output schema and full annotation coverage, the description provides sufficient context. It explains what is returned, what is excluded, and how pagination behaves, so no critical gaps remain.

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. The description adds value by explicitly warning not to pass start_index or items_per_page and clarifying that only company_number is meaningful, reinforcing the schema's ignored-parameter notes.

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 clearly states the action (Fetch active officers), the target resource (Companies House company number), and the scope (active officers, not resigned). This distinguishes it from sibling tools like company_profile and company_psc.

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 clear context about what is returned and excludes resigned officers. It explicitly instructs not to pass pagination parameters, but it does not name alternative tools or explicitly state when to use this tool over them.

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=true and destructiveHint=false, so the description adds value by detailing the return contents: status, registered address, SIC codes, filing compliance flags, and outstanding charges. It could mention potential null fields or that data may change, but the output schema covers return structure.

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 two sentences: the first declares the action, the second lists return contents and the prerequisite. No wasted words, tightly structured, and front-loaded.

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 one well-documented parameter, strong annotations, and an output schema, the description fully equips the agent. It clearly states what to expect in the output and how to obtain the required input, making it complete for the tool's complexity.

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 parameter, with a detailed description including format and example ('8 digits, e.g. 03782379'). The description reinforces that the company_number is obtained from company_search, clarifying the parameter's origin 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 uses the specific verb 'Fetch' and names the resource 'full Companies House profile' for a company number. It clearly distinguishes from sibling tools like company_officers or company_psc by enumerating profile-specific fields (status, registered address, SIC codes, compliance flags, charges).

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 explicitly instructs to 'Use company_search first to find the company number,' establishing a clear prerequisite and workflow. It does not explicitly mention when-not-to-use or alternative tools, but the use case is unambiguous given the tool's purpose.

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 the tool read-only, idempotent, and non-destructive. The description adds valuable behavioral details: it returns specific fields (natures of control, nationality, residence), flags overseas corporate PSC entries as a risk signal, and explains the special case for widely-held PLCs. No contradictions with annotations.

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 three sentences, front-loaded with the core purpose, and each subsequent sentence provides meaningful detail without fluff. No redundant phrases or irrelevant 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?

Given the tool's simplicity (one parameter) and the presence of an output schema, the description covers all essential behavior: what is returned, the risk flag, and the PLC edge case. It is complete for its complexity class.

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 input schema fully describes the only parameter (company_number), including format and example. The description does not need to elaborate further, matching the baseline of 3 when schema coverage is high.

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 ('Fetch') and resource ('Persons with Significant Control (beneficial ownership) for a company'), immediately distinguishing it from sibling tools like company_officers or company_profile. It clearly states the scope and returns a distinct data type.

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 provides clear context for when this tool is relevant—beneficial ownership and risk screening—by mentioning the overseas corporate PSC risk flag. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to select it over related company 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral details beyond annotations: it returns a paginated list with specific fields (name, number, status, SIC codes, incorporation date, registered address) and explains the pagination mechanism, which is valuable, non-obvious context.

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 three sentences, each earning its place: purpose, return behavior, and referral/pagination guidance. It is front-loaded and free of redundancy.

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?

Given the tool's search nature, the description covers essential aspects: what it searches, what it returns, how pagination works, and when to use a sibling tool (company_profile). The output schema presumably handles return structure details, so the description is 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?

Schema coverage is 100%, so baseline is 3. The description enriches this by clarifying 'query' as company name or keyword and by explicitly stating the pagination formula (start_index=start_index+items_per_page), which adds meaning beyond the schema's individual parameter descriptions.

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 clearly states it searches the Companies House register by company name or keyword, with a specific verb and resource. It also distinguishes itself from company_profile by explicitly directing users there for full records, making its scope 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?

The description explicitly names an alternative (company_profile) and provides an exclusion ('Use company_profile for the full record once you have the company number'). It also gives concrete pagination instructions (re-call with start_index+items_per_page), giving clear when-to-use 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?

Annotations already declare the operation read-only, idempotent, and non-destructive. The description adds the return contents (reasons, Act/section, period, company names) and reinforces the need for a pre-discovered officer ID, which is valuable context beyond the annotations.

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 two sentences: a clear action statement and a compact list of returns plus usage note. Every phrase contributes value with no redundancy.

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?

Given a single parameter with 100% schema coverage and an output schema present, the description fully captures the tool's purpose, prerequisites, and return contents. It is sufficiently complete for an agent to invoke correctly.

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 schema already covers officer_id with a full description including its source. The description repeats this linkage but does not introduce new parameter constraints or types, so the baseline of 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 clearly identifies the action ('Fetch') and the resource ('full disqualification record for a director') via officer ID. It distinguishes itself from the sibling disqualified_search by focusing on retrieval of a specific record rather than searching.

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 explicitly states the prerequisite workflow: 'Use disqualified_search first to find the officer ID.' This provides clear guidance on when to use this tool and points to the alternative tool for discovery.

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 establish read-only, idempotent, non-destructive behavior. The description adds key behavioral context beyond that: the company name query returns zero results (a known pitfall), and it reveals the return shape (names, DOBs, snippets, officer IDs) plus a pointer to disqualified_profile for follow-up. This exceeds the annotation baseline without being exhaustive about rate limits or matching nuances.

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 sentences, each earning its place: purpose, when-to-use, critical restriction, and return value summary. Front-loaded with the most important info, no redundancy or fluff. Perfectly sized for quick parsing by an agent.

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, has an output schema, and annotations cover safety. The description covers purpose, usage guidelines, a key anti-pattern, and a related-tool pointer. Pagination and limits are already captured in the schema, so nothing critical is missing. The context is complete for reliable invocation.

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%: each parameter has a meaningful description, including the alias relationship between 'name' and 'query' and clarifying 'NOT a company name.' The tool description reinforces this but doesn't introduce new parameter-level semantics. Baseline 3 is appropriate because the schema carries the burden.

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+resource: 'Check whether a named individual is banned from acting as a UK company director.' This clearly distinguishes the tool from siblings like company_search (which searches companies) and disqualified_profile (which retrieves full details for an identified officer). The scope is precise and 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?

Provides explicit when-to-use guidance: 'Use this tool when asked to check disqualified, banned, or barred directors.' It also warns against a common mistake: 'Query must be an individual's name — NOT a company name, which always returns zero results.' This exclusion is valuable and no other tool is suggested for this purpose, making the guidance direct and actionable.

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 read-only, idempotent, and non-destructive nature, so the bar is lower. The description adds useful behavioral context by explaining how the tool routes by prefix to the appropriate register, which is beyond the annotations. No contradictions.

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 succinct: a one-sentence purpose followed by a bulleted list of routing rules. Every sentence is informative, there is no unnecessary text, and the structure is front-loaded and scannable.

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 tool with an output schema and read-only annotations, the description covers all essential context: what it does, when to use it, and how routing works. It doesn't need to explain return values because the output schema exists, and the usage context is clear.

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 schema covers the parameter format 100%, so the baseline is 3. The description adds value by mapping each prefix to its target register, enriching the semantics beyond the schema's format description. This helps the agent construct valid IDs and anticipate the output type.

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 clearly states the verb and resource: 'Fetch the full record for an ID returned by search.' It distinguishes from sibling tools by specifying the routing mechanism by prefix, which clarifies that this is a unified fetcher across multiple registers. This is specific and 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?

The description gives a clear context: use this for IDs returned by search. It implicitly differentiates from specific profile tools via the prefix routing, but it does not explicitly state alternatives like 'use company_profile if you already have a company number.' A slight gap in explicit exclusion 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: the severity sort order, the specific notice codes covered, that each result includes notice_numeric_id, and the legal significance of a Gazette notice. This transparency is not contradicted by any annotation, making the description genuinely informative.

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 well-structured and front-loaded, with the core purpose in the first sentence. It uses four short paragraphs to cover coverage, sorting, result usage, and legal context. Every sentence earns its place, though the final paragraph on legal effectiveness, while useful, is slightly verbose. It remains appropriately concise for the tool's complexity.

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 has 7 parameters, an output schema, and annotations, so the description need not restate return structure. It nonetheless provides key operational details: the notice code list, severity ordering, and the notice_numeric_id link to full text. This is sufficient for an agent to select and invoke the tool correctly. The only gap is explicit sibling differentiation, which is minor given the clarity of the purpose.

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 input schema provides 100% parameter description coverage, including aliases and date format hints. The description does not deepen parameter-level semantics; it merely mentions 'entity name' and 'notice codes' at a high level. Per the baseline rule, with full schema coverage, a 3 is appropriate — the schema carries the parameter-documentation burden.

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: 'Search The Gazette's insolvency notice index by entity name.' It goes beyond a generic statement by enumerating covered notice codes (winding-up orders, administration orders, etc.) and the severity-based sorting, clearly distinguishing this from sibling tools like gazette_notice or company_search. This is a fully specific purpose statement.

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 clear context about how to use the tool: it searches the insolvency endpoint, covers specific notice types, and results are sorted by severity. It also explicitly directs users to the notice:// resource for full legal wording, which is a practical alternative. However, it does not explicitly contrast with when to use other search tools (e.g., company_search for non-insolvency company data), so it stops short of a 5.

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?

Beyond the readOnly and idempotent annotations, the description adds that the response is a complete JSON-LD record including parties, legal basis, court, and full text. It does not mention error handling or potential limits, but the key behavioral traits are well 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?

Two sentences efficiently deliver purpose, return contents, and a usage prerequisite. The structure is front-loaded with the action and avoids any redundant phrasing.

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?

Despite having an output schema, the description provides a succinct summary of return fields and gives the necessary upstream step. For a single-parameter fetch operation, this is complete and well-integrated with sibling tools.

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 schema already fully describes notice_id as the numeric ID returned by gazette_insolvency, and the description echoes this without adding new meaning. With 100% schema coverage, the baseline of 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 clearly states it fetches the full legal wording of a Gazette notice by numeric notice ID, distinguishing it from search-like siblings such as gazette_insolvency. The verb 'Fetch' and specific resource make the purpose 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 explicitly instructs users to use gazette_insolvency first to obtain notice_numeric_id values, providing a clear workflow and implicit alternative. This is direct guidance on when to use this tool versus another.

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 provided, the description carries the burden of explaining behavior. It discloses that the tool returns a rendered prompt as JSON with a messages array, and that arguments should be a dict mapping names to values. This provides useful behavioral context beyond the input schema, though it does not cover potential errors or permission requirements.

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 concise and front-loaded, with the core purpose in the first sentence and supplementary return/argument details in the second. Every sentence adds value without redundancy.

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?

Given the presence of an output schema and the simplicity of the tool (2 params, 1 required), the description is complete enough for an agent to understand the purpose, return format, and argument style. Minor gaps like error scenarios or relationship to list_prompts are not critical.

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 schema description coverage is 100%, so the parameters are already well-documented. The description adds a brief clarification that arguments are a dict mapping names to values, which aligns with the schema but does not introduce new semantic detail. Baseline of 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 clearly states the action ('Get a prompt by name') and specifies the resource ('a prompt') with optional arguments. It distinguishes itself from sibling tools like list_prompts, which would list available prompts rather than fetching a specific one by name.

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 implies usage when you know the exact prompt name and need its rendered content. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to select this tool for prompt retrieval by name.

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, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral context beyond these: it returns a maximum of 10 transactions, lists the exact data fields, restricts to England and Wales, and explains automatic postcode extraction from addresses. This is useful operational detail not present in the annotations.

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 sentences, front-loaded with the main action, followed by return details and key constraints. No filler or redundancy. Every sentence contributes meaningful guidance, making this highly concise and well-structured.

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 tool with a provided output schema, the description is complete: it states the data source, coverage area, reliability guidance, and expected return fields/count. There's no ambiguity that would require additional narrative. The output schema covers structural details, so the description's brevity is appropriate.

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 parameter 'address_or_postcode' is already described in the schema with the same guidance (postcode most reliable, full address accepted). The description adds a small extra detail about automatic postcode extraction, but with 100% schema coverage, the schema carries the semantic weight. Per the rubric, 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 opens with a specific verb+resource: 'Search HM Land Registry Price Paid Index by postcode or address.' It clearly distinguishes this tool from sibling tools by focusing on property transaction data, and states the return content (price, date, address, property type, tenure). This leaves no ambiguity about what the tool does.

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 gives clear contextual guidance: covers England and Wales only, postcode is most reliable, full address accepted with automatic postcode extraction. It doesn't explicitly name alternatives or exclusion conditions, but the guidance is sufficient for choosing this tool for price paid lookups. Score 4 rather than 5 because there's no explicit 'when not to use' statement.

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 of disclosing behavior. It states that the tool returns JSON with prompt metadata, including name, description, and optional arguments, which is transparent for a read-only list operation. It doesn't mention edge cases like pagination or errors, but the simple scope and output schema reduce the need.

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 two sentences with a clear front-loaded purpose statement and a concise return-value clarification. Every word contributes meaning, with no unnecessary filler.

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 zero-parameter tool with an output schema, the description is largely complete: it states what it lists and what the response includes. It could strengthen usability by explicitly directing users to get_prompt for single-prompt retrieval, but this is a minor enhancement rather than a 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?

The tool has zero parameters, so the description cannot add much parameter-specific meaning. The baseline for zero parameters is 4, and the description appropriately focuses on the returned metadata rather than input semantics.

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 ('List') with a clear resource ('all available prompts'), making the tool's purpose immediately obvious. It also differentiates from sibling tools like get_prompt by emphasizing 'all' prompts, signaling an enumeration behavior.

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 clearly implies use when an inventory of all prompts is needed. It doesn't explicitly mention alternatives like get_prompt for retrieving a single prompt, but the contrast between 'all' and the sibling name provides sufficient contextual 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?

The description adds rich behavioral detail beyond annotations: matching is deterministic and normalised (case/accent/punctuation insensitive), entity names match reliably but person names with transliteration variants may not, an empty result is not a guarantee of clearance, and false positives are possible. It also explains the meaning of 'lists_screened' and 'as_at' output fields. This substantially exceeds the annotations' readOnly/openWorld/idempotent hints.

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 well-structured and front-loaded: purpose, use case, matching behavior, caveats, and output fields. Every sentence contributes substantive information; there is no fluff or repetition. The length is justified by the complexity of the tool's behavior.

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?

Given the tool's complexity (sanctions screening with nuanced matching and result interpretation) and the presence of an output schema, the description covers all critical aspects: which lists are screened, matching details, interpretation of results, and meaning of key output fields. It is complete and self-contained.

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. The description adds semantics for the 'name' parameter by explaining how matching behaves (normalised exact/alias match, transliteration caveats), which is not in the schema. It also clarifies that 'entity_type' filters person vs entity, consistent with the schema. This adds value beyond the structured field descriptions.

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 clearly states the tool's function: 'Screen a name against the UK (OFSI), US (OFAC), EU and UN consolidated sanctions lists.' It uses a specific verb ('screen') and specific resource (sanctions lists), and is distinct from sibling tools which focus on company/gazette/land data. The title reinforces the purpose.

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 explicit usage context: 'Use it to check whether a counterparty — or its officers / persons with significant control — appears on a sanctions list.' It also provides important caveats about what the tool is not for: 'This is a screening aid, not a compliance determination.' While it doesn't name alternative tools (none exist among siblings), the when-to-use and when-not-to-use guidance is clear.

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?

Beyond the annotations (readOnly, idempotent), the description discloses that searches run in parallel and that results are IDs, not full records, requiring a follow-up fetch call. This adds meaningful behavioral expectations.

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 concise, structured in three sentences: what, scope, and how to use results. No redundant words or vague statements.

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 aggregate search tool, the description covers the essential elements: service scope, parallel behavior, and result handling. The output schema and annotations supply additional context, so the description is sufficiently 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 input schema already documents the query parameter thoroughly, including its type, length constraints, and semantic meaning. The description does not add additional parameter-level detail beyond what the schema provides, so a baseline score 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 clearly states the tool searches all UK due diligence registers simultaneously, naming the specific registers (Companies House, Charity Commission, disqualified directors, Gazette insolvency). This distinguishes it from sibling tools that target individual registers, making the purpose 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 specifies this is a cross-register search ('across all UK due diligence registers simultaneously') and that it returns IDs for use with fetch, implying the workflow. It doesn't explicitly state when not to use it or name alternatives, but the sibling tools provide that context.

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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful context: it returns the trading name and address, and notes that this often differs from the Companies House registered address, framing it as a due diligence signal. This goes beyond what annotations alone convey.

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 three short paragraphs, each earning its place: core purpose, return-value detail with a practical caveat, and an explicit scope limitation with an alternative. It is front-loaded, efficient, and free of fluff.

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?

This is a simple one-parameter tool with an output schema present. The description covers the return values, the UK-only scope, the external alternative, and a subtle due-diligence nuance. Nothing important is missing for correct selection and invocation.

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 input schema has 100% description coverage for the single parameter, including accepted formats and normalization behavior. The description itself adds no additional parameter-specific meaning, so the baseline score of 3 is appropriate; the schema already does the heavy lifting.

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 'Validate a UK (GB) VAT number against the HMRC register,' using a specific verb and resource while immediately clarifying the UK-only scope. This clearly distinguishes it from sibling search/profile tools that focus on company or charity data.

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 explicitly states 'Non-UK (EU) VAT numbers cannot be validated here — use the EU VIES service for other member states,' providing both an exclusion and a concrete alternative. For UK numbers, the implied use case is clear and unambiguous.

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