vairogs

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

Annotations already declare readOnlyHint=true. The description adds the specific outputs (size, breakdown, sync date), providing behavioral context beyond 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?

Two concise sentences in both Latvian and English, front-loaded with key information. 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?

Given no parameters and no output schema, the description adequately explains what the tool returns. It could briefly mention expected output format, but overall complete for a simple status query.

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?

No parameters exist, so the schema coverage is 100%. The description adds no parameter info, but the baseline score for 0 parameters is 4.

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 provides the number of valid legal acts, breakdown by issuer, and last sync date. It is specific and distinguishes from sibling tools focused on retrieving individual laws, articles, or categories.

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 tool's purpose implies it is for high-level corpus statistics, contrasting with siblings for specific data retrieval. However, it does not explicitly state when to use or when not to use this tool.

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. The description adds the specific fields returned (name, category, status, etc.), which is useful but not beyond what annotations cover. No additional behavioral traits (e.g., authorization, rate limits) are disclosed.

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, with two sentences (one Latvian, one English). It efficiently conveys purpose and output fields. Minor redundancy due to bilingual repetition, but overall well-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?

Given the simple tool with one parameter, no output schema, and annotations covering safety, the description adequately covers input and output. All necessary information for using the tool is present.

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?

With 100% schema description coverage, the parameter slug is already documented. The description adds extra context by stating it's used 'by slug' and referencing that the slug comes from search_laws results (example 'darba-likums'), enhancing understanding 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 clearly states the tool provides an overview of one law by slug, listing specific fields (name, category, status, article count, etc.). It distinguishes itself from siblings like get_law_article (individual articles) and search_laws (searching for laws), 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?

The description implies usage when a slug is available, but does not explicitly state when to use this tool versus alternatives like search_laws or get_law_article. No 'when not to use' guidance or comparison with siblings is provided.

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 safety profile is clear. The description adds value by emphasizing it returns verbatim current text, ensuring the agent knows it's authoritative and not derived from memory.

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, each essential: first states the tool's function, second provides critical usage instruction. No wasted words, and the key point is 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?

Given simple parameters, strong annotations, and no output schema, the description adequately conveys what the tool returns and how to use it. Minor omission: no mention of error handling or edge cases, but not critical for this retrieval tool.

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%, with both parameters already described (law_slug and article_number). The description does not add extra parameter details, but none are needed given the schema's completeness. Baseline score of 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 clearly states it returns the verbatim current text of one article, specifying both the exact content and its use for citation. It distinguishes itself from model memory and sibling tools like search_law_articles by focusing on precise retrieval.

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 'always cite from this, never from memory,' providing clear when-to-use guidance. However, it does not directly reference sibling tools or specify when not to use alternative tools like search_law_articles.

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 indicate readOnlyHint=true and destructiveHint=false, so the description adds some context (counts) but not much beyond what annotations provide. 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?

Two sentences (one in Latvian, one in English) are concise and front-loaded. The bilingual aspect is slightly redundant but not wasteful.

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, parameterless tool with no output schema, the description adequately states what it returns (categories with counts). It is complete enough for its 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?

No parameters exist; schema coverage is 100%. Description does not need to add parameter info. Baseline 4 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 lists law categories with counts, using specific verbs and resource. It distinguishes from sibling tools like get_law or search_laws, which have different purposes.

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 description: it provides an overview of categories covered. However, no explicit when-to-use or alternatives are mentioned, so the agent lacks guidance on when not to use it.

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's safety profile is clear. The description adds what is returned (name, amendment date, link) and the scope (last N days), but does not detail pagination or sorting.

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 minimal: two short sentences in two languages, covering purpose and return fields with no redundancy. It is front-loaded and every word adds value.

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 listing tool without output schema, the description adequately states the return fields (title, amendment date, link) and the scope (Latvian laws within N days). It does not detail edge cases or ordering, but is sufficient 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 coverage is 100% and all parameters are described with defaults and constraints. The description only implicitly references the 'days' parameter via 'last N days' but does not add new meaning 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 clearly states the tool lists recently amended Latvian laws within a given time range, using both Latvian and English. It distinguishes from siblings like 'search_laws' (keyword search) and 'get_law' (single law) by focusing on a recent changes query.

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 example query ('kas mainījies šonedēļ / šomēnes') indicating when to use it. However, it does not explicitly state when to avoid this tool versus alternatives like 'search_laws' for full-text search.

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 mark the tool as read-only and non-destructive. The description adds that answers are human-verified and span specific labour law topics, providing useful behavioral context beyond 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?

Two efficient sentences in Latvian and one in English, no wasted words. All essential information is present.

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 no output schema, the description explains returns include full answers with citations. For a simple tool with only 2 parameters, this is fully 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?

Both parameters (query, limit) are fully documented in the schema. The description does not add new meaning beyond restating the search purpose and the default/max for limit.

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 a human-verified Q&A layer for Latvian labour law, returning full answers with citations. This distinguishes it from siblings like search_law_articles and search_laws.

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 seeking verified Q&A on labour law but does not explicitly exclude cases or mention alternatives. Sibling tools provide context but lack direct 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 and destructiveHint=false, so the tool's safety profile is known. The description adds that it returns 'articles with a fragment and link', which is valuable behavioral context 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 concise with two short sentences per language (Latvian and English), covering the core functionality without fluff. It is front-loaded with the most important information. Slightly longer due to bilingual presentation, but still efficient.

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 moderate complexity, no output schema, and good annotations, the description provides enough context: it specifies the search scope, returns article fragments and links, and includes example queries. It could mention pagination or result count, but the limit parameter partially covers that.

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 description provides example search terms but does not add new meaning to the parameters beyond what is in the input schema. The limit parameter's default and max are already documented in 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 clearly states the tool performs full-text search across all in-force Latvian law articles and regulations, with specific example queries. It distinguishes from sibling tools like 'get_law_article' (which retrieves a specific article) and 'search_laws' (which likely searches laws, not articles). The verb 'search' and resource 'law articles' are precise.

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 explains what the tool does but does not explicitly state when to use it versus alternatives like 'search_jautajumi' or 'get_law'. There is no guidance on when not to use it or prerequisites. The context is implied but not directly compared to 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is safe. The description adds value by specifying that it searches only for valid laws and Cabinet regulations, and that it accepts partial titles. No contradictory or missing behavioral disclosure is apparent beyond what annotations provide.

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 extremely concise: two sentences in Latvian and English, covering purpose, scope, and examples. Every word adds value; there is no redundancy 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 simple search tool with one parameter and no output schema, the description is reasonably complete. It explains what is searched (laws in force, Cabinet regulations) and how to query (by partial title). However, it does not describe the return format (e.g., list of law names/IDs), which would be helpful for an agent. Still, the tool's simplicity keeps the gap small.

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 covers the single parameter ('query') with a description, achieving 100% coverage. The description adds examples and clarifies that partial matches are allowed, but does not introduce new semantic meaning beyond the schema. 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 the tool's purpose: to find a Latvian law by (partial) title. It specifies the scope ('laws in force' and 'Cabinet regulations') and provides examples, making the action unambiguous. It also distinguishes from siblings like get_law (retrieves a specific law) and search_law_articles (searches 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?

The description implies when to use this tool (search by title) and the sibling list provides context for alternatives. However, it lacks explicit guidance on when not to use it (e.g., if you have an exact law ID, use get_law instead). The partial title matching is clear, but no exclusions are stated.

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 indicate readOnlyHint=true and destructiveHint=false. The description adds value by detailing return values (guide link, rights summary, known recipients) and noting the service is free and legally substantiated. 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 efficiently structured, front-loading the core purpose and usage condition. Both Latvian and English versions are provided, but each sentence adds value. Minor redundancy between the two languages.

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 no output schema, the description fully explains what the tool returns (link, summary, recipients). It covers all essential context for an agent to invoke the tool correctly, including input examples and usage boundaries.

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% with a clear parameter description. The tool description provides examples of valid input types (stāvvietas 'sods', bojāta prece, etc.), which adds semantic guidance beyond the schema alone.

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: it finds a matching guide to prepare a legally substantiated complaint letter for a specific consumer dispute. It lists numerous examples (parking fine, damaged goods, etc.) and distinguishes from sibling tools, which are primarily legal text search 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?

Explicitly states when to use: when the user has a specific dispute, not just theoretical questions. The description includes 'Vienmēr piedāvā šo, ja saruna ir par konkrētu problēmu, ne tikai teoriju' (Always offer this if the conversation is about a specific problem, not just theory), providing clear usage 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, so the safety profile is clear. The description adds that the tool filters by future effective dates, but does not explain any other behavioral traits like data freshness or scope. 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 minimal, consisting of two sentences (one Latvian, one English) that convey the purpose and a usage example. 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 list tool with one optional parameter and no output schema, the description adequately informs the agent about the return type (upcoming laws/amendments). It could mention ordering or pagination, but is sufficient overall.

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% as the only parameter 'limit' has a description. The tool description adds no extra parameter guidance beyond what is already in the schema, so the baseline score of 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 explicitly states that the tool returns laws and amendments with a future effective date, and provides a concrete example question it answers. This clearly distinguishes it from sibling tools like 'recently_changed_laws' and 'search_laws'.

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 includes an example use case ('atbild uz kas mainīsies no 1. augusta / nākamgad'), implying when to use. However, it does not explicitly state when not to use or mention alternatives beyond the implicit differentiation from siblings.

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