vairogs

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

Annotations already declare readOnlyHint=true and destructiveHint=false, establishing the safety profile. The description adds meaningful context about what data is returned (counts by issuer, sync date), but does not disclose any additional behavioral traits such as potential external calls, rate limits, or data source behavior. Since annotations cover the main safety aspects, this is adequate but not exceptional.

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 concise sentence in Latvian, followed by an English summary. It is front-loaded with the core purpose (how many current acts), then adds breakdown and sync date. There is no wasted wording, and the bilingual format is clean and 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?

For a simple status tool with no parameters and no output schema, the description provides adequate context: it specifies the type of data (count, breakdown by issuer, last sync date) and gives examples of issuers. It does not describe the exact response structure, but given the simplicity and lack of output schema, this is sufficient. It is slightly less complete than ideal because it doesn't mention whether the sync date is for the whole corpus or per issuer, but that is a minor 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 baseline for this dimension is 4. The description does not need to explain parameter semantics, and the input schema is empty. The description focuses on output semantics, which is appropriate for a parameterless tool.

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 what the tool does: reports the count of current legal acts in the Vairogs database, broken down by issuer, and the last sync date with likumi.lv. It uses specific verbs and resources ('Cik spēkā esošu tiesību aktu ir Vairoga datubāzē') and distinguishes itself from sibling tools, which focus on retrieving individual laws, articles, or searches.

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 context is implied: an AI agent would understand to use this tool when needing corpus statistics or data freshness, but the description does not explicitly state when to use it versus alternatives or provide exclusions. No sibling tool comparisons are given, so guidance is limited to what is implied by 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 readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds value by listing the returned data fields (name, category, status, article count, last sync, links), which goes beyond annotations and clarifies the output.

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 bilingual sentence that front-loads the tool's purpose and output fields. It is concise, with no filler or redundant content beyond the bilingual presentation, which is appropriate for the Latvian context.

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 a single parameter, no output schema, and low complexity. The description adequately covers what the tool does and what it returns, making it complete for its scope. No additional behavioral details are necessary.

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 only parameter 'slug' with a clear explanation ('from search_laws result'), and schema description coverage is 100%. The description merely repeats that it works 'by slug' without adding further meaning, so the baseline 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 the tool's function: 'Overview of one law by slug' and enumerates the specific fields returned. This distinguishes it from sibling tools like get_law_article, which retrieves an article, and search_laws, which searches for 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 the usage context: you need a slug from a prior search_laws result, as indicated in the parameter description. This gives clear context but does not explicitly state when not to use it or mention alternatives, so it stays at 4.

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 and non-destructive hints. The description adds meaningful context: it returns the verbatim current text, implying source-of-truth and up-to-date content, and warns against relying on model memory. This goes beyond the annotation fields.

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 concise sentences (bilingual) that immediately state the core function and usage rule. Every word earns its place, with no filler or redundant content.

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 two clearly described parameters, the description plus schema and annotations cover the essential context: what it does, when to use it, and expected output (verbatim full text). No output schema exists, but the description already states the return semantics, making the tool 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?

Schema description coverage is 100%, with both parameters (law_slug and article_number) fully described. The description does not add extra parameter-level detail, but it is unnecessary given the schema's clarity. 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 the tool retrieves the full verbatim text of one article from the current official version. It uses a specific verb (get/retrieve) and resource (law article), and the emphasis on 'never from memory' distinguishes it from model memory and from sibling search tools like search_law_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 gives explicit guidance to use this tool for citation purposes and never from memory. It provides a clear context for when to use, though it does not explicitly name alternatives or exclusions beyond 'not memory'.

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 read-only nature is covered. The description adds useful context by specifying 'in force' (Spēkā esošo) and 'with counts', indicating the scope and return content beyond what annotations provide. It does not detail return format or ordering, but the added context is valuable.

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, consisting of two short sentences (Latvian and English) that directly convey the tool's purpose. It is front-loaded with the key verb and resource, and every word 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 tool's simplicity (no parameters, no output schema), the description fully covers what the tool returns: categories with counts, limited to in-force laws, and serving as an overview of sectors. The annotations handle safety, and the description covers return value semantics, making it complete for this context.

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?

There are zero parameters, so the baseline is 4. The description correctly avoids explaining parameters since none exist, and the schema already reflects this. No additional parameter semantics are needed.

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 a specific verb ('list') and resource ('law categories'). It distinguishes itself from sibling tools like search_laws or get_law by focusing on a category-level overview rather than individual 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 this tool is for getting an overview of database coverage by sector, which suggests using it before drilling into specific laws. However, it does not explicitly mention alternatives or when not to use it, so it falls short of a perfect score.

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 known. The description adds behavioral value by specifying the return fields (name, amendment date, link) and the scope (laws and Cabinet regulations with amendments in force), which is helpful given no output schema exists.

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 compact and front-loaded: the first sentence states the exact purpose, and the second provides the return format. Bilingual but efficient with no redundant 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?

The tool is simple, with two optional parameters and strong annotations. The description covers purpose, return fields, and resource scope. It could mention sorting or pagination, but these are not critical for a basic list tool, and the presence of annotations and full schema coverage raises the baseline.

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%, with both days and limit already documented with sensible descriptions. The description only reiterates the time-window concept via 'last N days' and does not add further meaning beyond the schema. 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 identifies the tool as listing recently amended Latvian laws (including Cabinet regulations) within a specified time window, using a specific verb and resource. It answers the question 'kas mainījies šonedēļ / šomēnes' and distinguishes itself from sibling tools like search_laws or get_law.

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 the tool: to find laws with recent amendments, answering 'what changed this week/month'. It implies the use case but does not explicitly name alternatives or exclusion criteria, though the sibling context makes the distinction obvious.

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 read-only and non-destructive. The description adds that results are human-verified and include article citations, and that current coverage is Labour Law topics. This provides useful 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 (Latvian/English) with no fluff, though bilingual repetition is slightly redundant. It front-loads the action and scope.

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, with good annotations and schema. Description explains the search scope, the nature of answers (verified with citations), and the return format. No output schema is needed given the clear description.

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 already fully documents both parameters (query and limit) with descriptions in Latvian. Description adds no additional parameter-specific detail, so 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?

Description clearly states it searches a human-verified Q&A layer with specific scope (Labour Law) and returns full answers with citations. This distinguishes it from sibling law-article/law search tools 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?

Provides clear context: use for verified Latvian labour-law Q&A. It implies this is for common questions with verified answers, but does not explicitly contrast with alternatives such as search_law_articles or search_laws, so it lacks explicit exclusions.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds that it searches only 'in-force' articles and returns 'pants ar fragmentu un saiti' (articles with fragment and link), disclosing the output form. This goes beyond annotations and informs the agent about behavior.

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 in a bilingual format, front-loaded with the core purpose. Every sentence contributes meaning: scope, examples, and return format. No filler or 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?

With 2 simple parameters and no output schema, the description covers the essential aspects: what it searches, scope, examples, and return shape. The only notable gap is lack of explicit differentiation from sibling search tools, but the core functionality is well-described.

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 query and limit described. The description adds value by providing example search phrases and clarifying the full-text scope, helping the agent understand what kind of queries are appropriate. Since the schema already covers parameter details, the examples provide marginal but useful enrichment.

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 is a full-text search tool for all in-force Latvian law articles and MK regulations, with concrete example queries. This specific verb+resource+scope distinguishes it from sibling tools like get_law_article (fetch specific article) and search_laws (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: it is for full-text search across articles, with examples of legal terms to search. However, it does not explicitly contrast with alternative tools like search_laws or search_jautajumi, so the agent receives no direct guidance on when to prefer this over 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 indicate read-only and non-destructive behavior, and the description adds context by restricting results to 'spēkā esošu' (valid) laws and supporting partial title matches. It does not describe return format or error behavior, but the annotations already cover safety.

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 (Latvian with English translation), directly front-loaded with the core action and examples. No unnecessary detail or repetition of schema contents.

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 adequately complete: it specifies the input type and the scope of results. It does not explain return format or failure handling, but given the tool's simplicity and read-only annotation, this is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (the query parameter is described as 'Law title or part thereof'). The description enriches this with two concrete examples and clarifies that partial matching is supported ('vai tā daļas'), adding practical usage context beyond the raw 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's function: 'Atrod spēkā esošu likumu vai MK noteikumus pēc nosaukuma' (finds valid laws or Cabinet regulations by title), with concrete examples. It distinguishes from siblings like search_law_articles by explicitly targeting laws by title, not 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 the tool (when you have a full or partial law title), but does not explicitly state alternatives or when not to use it. Sibling tool names suggest related tools (e.g., get_law, search_law_articles), but the description itself provides no exclusionary 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. The description adds valuable context: that it returns a link, rights summary, and known recipients, and that the guide is free. It does not mention edge cases like no match found, but the core behavior is transparent.

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 with the condition upfront, then function, return details, and a usage directive. It includes a redundant English translation but every sentence earns its place; it's not overly verbose.

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 only one parameter and no output schema, the description covers when to use, what it does, what it returns, and provides examples. It is fully sufficient for an agent to select and invoke the tool 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 describes 'situacija' as a free-text Latvian situation description, giving 100% coverage. The description enriches this by listing concrete examples (parking fine, damaged goods, subscription, etc.), which helps the agent understand what qualifies as a 'specific dispute.'

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: for a specific dispute, it finds a matching Vairogs complaint-letter wizard, returns a link, rights summary, and known recipients. It uses specific verbs and distinct resource ('vairogs.lv complaint-letter wizard'), differentiating it from sibling law-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?

The description explicitly says when to use: 'Kad lietotājam ir konkrēts strīds vai sūdzība' and instructs 'Vienmēr piedāvā šo, ja saruna ir par konkrētu problēmu, ne tikai teoriju.' This gives clear context and implies alternatives for theoretical queries.

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 covered. The description adds the future-effective-date filter and intent, but it does not disclose ordering, pagination defaults, or response shape beyond the schema's limit parameter.

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 short clauses in Latvian and English, front-loaded with the object and scope. It contains no redundant filler and is immediately 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 one-parameter list tool with read-only annotations and a clear output concept (laws and amendments), the description fully explains what it returns and when to use it. No output schema is needed for a simple list of this kind.

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%—the limit parameter has its default and maximum documented. The description adds no additional parameter guidance, but none is needed given the schema already provides the essential 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 clearly identifies the resource (Latvian laws and amendments) and the scope (future effective date), distinguishing it from recently_changed_laws by temporal direction. It also provides a concrete query it answers, such as 'what changes from August 1 / next year'.

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 use case and implies it should be used for future-dated changes rather than past changes. It does not explicitly name alternatives or exclusions, but the sibling tool context (e.g., recently_changed_laws) reinforces the distinction.

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