Regulations.ai — Global AI Law Tracker

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint=false), so little is needed here. The description adds what comes back — facts plus a snippet and an external regulations.ai link — which is useful but it says nothing about permissions, pagination, or failure modes when ids are missing.

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?

One sentence, no filler, with the cardinality constraint and return contents front-loaded. Every clause 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?

With no output schema, the description must carry the return shape and does so at a high level (per-regulation facts, snippet, full-comparison URL). It is nearly complete for a simple idempotent read, missing only edge-case behavior such as how it handles invalid or fewer-than-two ids.

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

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

Schema description coverage is 100% and the single parameter is already documented as '2 to 5 regulation ids'. The description restates the same 2-5 constraint without adding format, ordering, or semantics beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb-resource pairing ('return regulations side by side') and the cardinality (2-5). An agent can distinguish it from the single-item 'get_regulation' or 'search_regulations' by the 'side by side' framing, though it never explicitly names a sibling.

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 2-5 range (use this to compare several regulations), but there is no explicit when-to-use, when-not-to-use, or routing to alternatives such as get_regulation or search_regulations.

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, idempotent, non-destructive behavior, so the bar is lower. The description adds genuinely useful disclosure beyond them: it returns key facts plus a summary, and it carries a mandatory presentation instruction to always show the user the regulations.ai url.

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 tight clauses, no filler, and the identifying purpose is front-loaded before the return-value and url guidance. 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?

With no output schema, the description usefully characterizes the return payload (facts, summary, url) and mandates surfacing the url to the user. It is close to complete for a single-param read tool; only failure/error behavior for an invalid id is unspecified.

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 has 100% description coverage and only one parameter, and the description merely restates that the id is one returned by `search`. Per the baseline rule, 3 is appropriate when the schema already carries the parameter 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?

States a specific verb (Retrieve), resource (one document), and scope (one returned by `search`, addressed by id). It names the sibling tool that produces the id, so an agent can immediately distinguish it from `search`, `get_regulation`, and `search_regulations`.

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?

Clearly establishes the precondition that the id must come from `search`, which routes the agent correctly. It does not state when not to use it or contrast directly with `get_regulation`/`search_regulations`, so it stops short of full when/when-not 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 readOnly, idempotent, non-destructive, and closed-world behavior, so the safety profile is covered. The description adds genuine value beyond that by disclosing that definitions are abridged on the free tier and that the response includes a url — a real behavioral/quality caveat an agent should know.

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, front-loaded with the lookup action and immediately followed by the return shape and the free-tier caveat. 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 no output schema, the description usefully enumerates the return (definition and url) and the free-tier abridgement caveat for this simple single-parameter tool. Only the usage-vs-siblings gap keeps it from being 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?

There is a single required parameter with 100% schema description coverage (including an example value), so the schema already carries the semantics. The description adds nothing about the term parameter beyond what the schema provides, making the baseline 3 correct.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (look up) and resource (term from a 4,000-entry glossary) and names the return payload (definition + url). This clearly distinguishes it from siblings like get_regulation or search_regulations, which operate on regulations rather than glossary terms.

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 never says when to use this tool versus the siblings (get_regulation, compare_regulations, search), nor does it name any alternative or exclusion. Usage is only implied by the title and the term-based parameter.

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, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuine behavioral context beyond that: it discloses that only metadata plus a snippet are returned and that the complete analysis lives on the returned url, which shapes how the agent should relay results.

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, zero filler, with the retrieval scope front-loaded and the output-handling instruction second. Every clause 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?

No output schema exists, yet the description enumerates the returned fields and directs the agent to the url for the full analysis, so return-value behavior is sufficiently covered. Combined with annotations covering safety and idempotency, nothing needed to call this correctly is missing.

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 single id parameter, so the baseline would be 3. The description adds provenance meaning by specifying the id is 'as returned by search_regulations', telling the agent where a valid value comes from rather than just naming the field.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (fetch) and resource (one regulation) and explicitly enumerates what comes back: jurisdiction, status, type, dates, official source and a snippet. The parenthetical '(as returned by search_regulations)' ties it to the sibling that produces the id, so an agent can distinguish it from search_regulations or compare_regulations immediately.

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?

Mentions the id comes from search_regulations, which implies the correct upstream workflow, and instructs the agent to point the user to the returned url for the full analysis. It gives clear context but states no explicit when-not condition or case where another sibling would be preferred.

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, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds only the return payload detail (a count per jurisdiction), which is modest added 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?

One sentence, front-loaded with the verb and resource, and the return detail is appended efficiently. No filler or restated boilerplate.

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?

There is no output schema, so the description carries return-value disclosure, and it does identify the two fields (jurisdiction, regulation count). For a zero-parameter read-only listing tool this is close to sufficient; only ordering, pagination, or total-count behavior is unaddressed.

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

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

The tool takes zero parameters, so schema coverage is trivially 100% and the baseline for a no-param tool applies. There is nothing further the description could say about parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ("List every jurisdiction covered") plus the payload shape ("how many regulations each has"), which is more than the title alone conveys. It does not name or contrast itself against siblings, but the siblings (search, fetch, get_regulation) are so different in kind that the resource alone is distinguishing.

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 no when-to-use, prerequisites, or alternatives. An agent must infer that this is a discovery/enumeration entry point used before calling get_regulation or search_regulations; nothing in the text states that routing.

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, closed-world behavior, so the bar is lower. The description adds genuinely useful context not in the annotations: the corpus is rescanned weekly, which tells the agent why freshness-dependent queries resolve here.

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, front-loaded with what it returns. The second sentence is slightly promotional ('the differentiated capability') but still earns its place by conveying the weekly-rescan justification.

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 low-complexity, zero-required-parameter read tool with full schema coverage, annotations covering the safety profile, and no output schema to explain, the description is nearly sufficient. Only ordering/pagination semantics beyond the schema's limit param are unaddressed.

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

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

Schema description coverage is 100% (days, limit, jurisdiction all documented with defaults and tier caps), so the schema carries parameter semantics. The description adds no additional syntax or format detail, making 3 the correct baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: regulations that were added or updated recently. An agent can tell it apart from vanilla search_regulations in substance, though it never names the sibling to clarify the boundary.

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 second sentence implicitly says when to use it — when a general model can't answer because the corpus is rescanned weekly — but there is no explicit when-not or named alternative such as search_regulations or get_regulation.

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, non-destructive and closed-world behavior, so safety is covered. The description adds the result shape ('id, title and url for each hit') and the intended handoff to fetch, which is genuine 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?

Two sentences, no filler. Corpus scope is front-loaded and the return/handoff detail follows, so an agent gets the important information first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description helpfully states the returned fields and the next-step tool, which is exactly what is needed. It stops short of covering result limits, pagination, or ranking behavior, so it is strong but not exhaustive.

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?

A single parameter with 100% schema description coverage, so the schema carries the semantics. The description adds nothing about query syntax, matching behavior, or result limits, making it the baseline case where the schema 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?

States a concrete verb and resource ('Search everything on regulations.ai') and enumerates the corpus types covered (laws from 160+ jurisdictions, glossary, enforcement, papers, news). The word 'everything' implicitly distinguishes it from the narrower sibling search_regulations, but that sibling is never named, leaving the agent to infer the boundary.

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 follow-on path ('pass an id to `fetch` for the summary'), which is useful routing. However it offers no guidance on when to pick this tool over the near-identical sibling search_regulations, which is the highest-risk selection decision here.

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, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds valuable behavioral context beyond that: what each result contains (key facts plus a short snippet) and the requirement to surface the regulations.ai url to the user.

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, zero waste: the first sentence covers capability and filters, the second covers the return payload and the user-facing url obligation. Well 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 no output schema, the description usefully explains the return shape (facts, snippet, url) and notes the free-tier limit indirectly via the schema. It is complete for a search tool, though pagination behavior and what happens when nothing matches are unaddressed.

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

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

Schema coverage is 100% and each parameter already carries a description, so the schema does the heavy lifting. The description restates the same facet names (keyword, jurisdiction, status, type) without adding syntax, format, or default-value detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Search AI regulations across 160+ jurisdictions by keyword, jurisdiction, status or type.' The breadth claim and field list make it clearly distinguishable from get_regulation (single record) and compare_regulations (comparison).

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 field list and the 'always give the user that url' directive, but there is no explicit when-to-use or when-not-to-use guidance, and no mention of alternatives such as get_regulation for a known record or compare_regulations for side-by-side analysis.

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