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

With no annotations, the description carries the full burden. It discloses the core behavior (searches across domains and returns regulations) and the 'quick' nature, but does not elaborate on output format, limitations, or whether it's read-only. This is adequate for a simple tool but not rich in behavioral detail.

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 a secondary clarifying sentence. Every word earns its place, and it avoids redundancy with 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 one-parameter tool with a well-explained parameter and no output schema, the description fully covers purpose, usage, and expected outcome. It also hints at scope ('all domains'), making it complete for the agent to select and 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?

Schema coverage for the only parameter is 100%, with examples and clear guidance ('Describe your system, product, or situation'). The description echoes this but adds little new meaning beyond the schema, justifying the baseline 3.

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: 'Quick compliance check' with a specific verb ('get relevant regulatory requirements') and resource (system/situation). It also distinguishes from siblings by noting it 'combines search across all domains', separating it from more targeted tools like query_regulation.

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 to use it ('Quick compliance check', 'describe your system or situation'), implying it is a broad, fast assessment. However, it does not explicitly name alternatives or state when not to use it, which would merit 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?

With no annotations provided, the description carries the full burden. The verb 'read' clearly implies a non-destructive, read-only operation, and the description adds useful workflow context. However, it does not mention potential error cases (e.g., missing slug), rate limits, or what exactly 'full content' includes beyond what a user might expect. Still, for a simple fetch-by-slug tool, the core behavioral trait is clear.

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 long, front-loaded with the primary purpose, followed by a precise usage guideline. Every word earns its place; no fluff or repetition of schema fields.

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 (single slug parameter, no output schema), the description covers the core functionality and usage flow. It lacks explicit note about return format or errors, but for a straightforward read operation, the essential information is present. The description is sufficiently complete for an agent to select and invoke it 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 input schema already has excellent parameter descriptions with examples, so the baseline is 3. The description adds extra meaning by explaining that the slug is obtained from search_wiki, which clarifies the parameter's origin and usage. This goes beyond what the schema 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 the specific verb 'Read' with a clear resource: 'the full content of a specific wiki article by its slug.' It explicitly distinguishes itself from search_wiki by positioning it as a follow-up step, and from list_articles by emphasizing 'full content.' This makes the tool's purpose unmistakable.

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 explicit workflow: 'Use search_wiki first to find relevant slugs, then get_article to read the full content.' This states when to use it and implicitly when not to use it (not for searching/finding articles). It names the primary alternative tool directly.

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 transparency burden. It discloses the read-only nature (list) and specifies return fields (title, slug, summary, domain). It does not mention potential pagination, sorting, or permissions, but these are not critical for a simple, safe listing operation.

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, well-structured sentence that leads with the action, then the optional filter, then the return value. Every word contributes and there is no fluff or repetition.

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 simple nature (one optional param, no output schema), the description is complete: it explains the main action, the filter option, and the return fields. No crucial missing context is evident.

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 covers the only parameter 'domain' with an enum and description. The description adds minimal extra meaning beyond restating 'compliance domain' and the optional filtering behavior, 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 uses a specific verb 'List' with a clear resource 'wiki articles', and indicates optional filtering by domain. It distinguishes itself from siblings like search_wiki (searching) and get_article (retrieving a single article) by explicitly listing all articles.

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 clear context: use to list all articles, optionally filtered by compliance domain. It doesn't explicitly contrast with alternatives or state when not to use, but the 'list all' scope clearly differentiates it from search/retrieval siblings.

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 must carry the full burden. It discloses return behavior ('Returns relevant articles with key points and cross-references') but does not explicitly state read-only nature or any side effects, permissions, or limitations. The absence of such details is acceptable for a query tool but leaves some ambiguity.

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 long, front-loaded with the main action, and contains no filler. Every word contributes to explaining the tool's purpose and output.

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 tool with a single parameter and no output schema, the description sufficiently explains what it returns and the scope of content. It could be more complete by explicitly noting it is read-only or how to best phrase questions, but overall it is adequate for 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?

The schema description covers the single parameter 'question' with examples, achieving 100% coverage. The description does not add much beyond the schema and simply restates the purpose, so a baseline score 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 a specific verb ('Ask') and resource ('compliance question... knowledge base'), and specifies the covered regulatory domains (GDPR, EU AI Act, CCPA/CPRA, software licensing, web accessibility). This distinguishes it from generic search tools like 'search_wiki'.

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 provides clear context for when to use the tool: when you have a compliance question. It does not explicitly name alternatives or exclusions, but the domain-specific phrasing gives sufficient guidance for typical use cases.

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. It discloses that the tool 'returns ranked results with relevance scores and match context', but does not state whether it is read-only, any rate limits, or behavior on empty queries. This is minimal but acceptable for a simple search 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?

The description is two sentences, front-loaded with purpose and return information, and includes a useful filtering note. Every sentence earns its place with no wasted words.

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 search tool with 3 params and no output schema, the description covers what it does, what it returns, and the domain filter. It could be improved by explicitly mentioning read-only nature or contrasting with siblings, but it is adequate for an agent to select and 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?

Schema description coverage is 100%, so all parameters are already documented. The description adds marginal value by mentioning 'Filter by domain for targeted searches', which reiterates the domain parameter, but does not introduce new semantics. 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 clearly states 'Search the compliance wiki for articles matching keywords' with a specific verb and resource. It distinguishes from sibling tools by focusing on keyword search, while siblings like get_article or wiki_stats serve different functions.

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 for finding articles via keywords and mentions domain filtering, but does not explicitly state when to use this tool versus alternatives. No alternative tools are named or exclusions are listed, making it implicit rather than explicit.

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 must carry the full burden. It clearly indicates a read-only statistics operation and names the returned metrics, but does not disclose response format, authentication needs, or any potential limitations. This is adequate for a simple stats tool, but not richer than necessary.

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 that immediately identifies the tool's action and resource, then enumerates the key statistics. Every word adds value, with no repetition of the name or 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 tool with no parameters and no output schema, the description covers the essential context: what the knowledge base is, what statistics are provided, and that it includes a generation date. It is sufficiently complete for a simple stats tool, though it could optionally mention whether output is a single object or an array.

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, and the schema is empty. The description adds meaning by specifying exactly what statistics are generated, which the schema cannot convey. With 0 params, baseline is 4, and the description meets that bar by providing functional context.

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 purpose: getting statistics about the compliance knowledge base. It lists specific metrics (article counts, word counts, domains covered, generation date) and is distinct from sibling tools which handle queries, searches, articles, and compliance checks.

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 this tool is for obtaining aggregate statistics, but it does not explicitly state when to use it versus alternatives like list_articles or search_wiki. No exclusions or alternative guidance is provided, leaving the usage context to inference.

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