AI Law Tracker

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) indicate safe, side-effect-free operation. The description adds 'deterministic, no LLM — cannot hallucinate', 'reproducible', and 'NOT legal advice', providing valuable 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?

Three sentences: defines the engine, summarizes the function, and adds constraints/disclaimer. Every sentence is essential and front-loaded with the most critical information.

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

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

The description covers the tool's purpose, deterministic nature, required parameters, paid behavior, and disclaimer. Given 9 parameters and no output schema, missing details about the response format are a minor gap, but overall adequate.

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 56%, so baseline is 3. The description only repeats that 'state' and 'sector' are required, which is already in the schema. No additional parameter-level guidance is provided.

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 'Give a business profile; get the applicable AI-law obligations plus a reproducible 1–10 risk score with a named factor breakdown.' It clearly identifies the tool as a deterministic compliance-report engine, distinct from siblings like get_ai_obligations which only list obligations without scoring.

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 comprehensive compliance assessment, mentioning paid feature differences and a legal disclaimer. However, it does not explicitly state when to use this tool versus siblings like 'get_ai_obligations' or 'generate_compliance_report'.

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?

Discloses that key is emailed and returned, plus one-per-email limit. Annotations show non-read-only and non-destructive, so description adds useful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no redundancy. Every sentence provides necessary information: action, delivery, return, and constraint.

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 tool simplicity (1 param, no output schema), description adequately covers purpose, behavior, and constraints. No gaps 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?

With 100% schema coverage and single parameter 'email', description adds value by explaining the email's purpose and that key is returned, supplementing the schema's minimal description.

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 explicitly states verb 'mint' and 'email' with resource 'free API key', clearly distinguishing it from sibling tools which focus on AI compliance and law queries.

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 constraint 'one active free key per email' but does not provide explicit guidance on when to use vs alternatives. Context sufficient for its simplicity.

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

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

The description goes well beyond the annotations (readOnlyHint=false, openWorldHint=true, idempotentHint=true, destructiveHint=false). It discloses that the report is deterministic with 'no LLM in the grounded path' (cannot invent statutes), that every obligation is anchored to real law, and that it is informational only, not legal advice. It also explains tier-specific behavior (preview vs. full report). There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, coherent paragraph of about seven sentences. It front-loads the primary purpose and includes critical disclaimers (no LLM, not legal advice) and tier behavior. While it is not excessively long, it could be more structured (e.g., bullet points for tiers) to improve scanability. It earns its sentences, but minor structural improvements would boost usability.

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

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

Given the tool's complexity (12 parameters, no output schema, tiered access), the description covers all essential behavioral aspects: return values (risk score + download URL), tier limitations, deterministic nature, legal grounding, and disclaimers. It provides sufficient context for an agent to correctly invoke the tool and interpret results. The absence of an output schema is compensated by explicit description of the output contents.

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 67%, but the description adds value by explicitly naming `state` and `sector` as required and noting that `jurisdiction` is an alias for `state`. This provides clarity beyond the schema, which already marks them required. However, the description does not detail other parameters like `ai_policy` or `bias_audit`, so it doesn't fully compensate for missing schema descriptions. The alias information earns a 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 generates a 'personalized, source-grounded AI-law COMPLIANCE REPORT' for a business profile. It distinguishes itself from sibling tools by noting it is the same deterministic engine behind the $79 report product and that it returns a risk assessment and download URL. This specificity meets the 'specific verb+resource' criterion and differentiates it from other list/get 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 provides clear context on when to use the tool (for generating a comprehensive compliance report) and includes important caveats: it is a paid feature (Pro+), and lower tiers only get a preview. While it doesn't explicitly state when not to use it or name alternative siblings, it implies the tool's purpose versus component tools like get_ai_obligations. This is clear but lacks explicit exclusion guidance.

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

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

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context: deadlines are derived deterministically, no LLM, no invented dates, and undated entries are not given invented dates. This goes beyond annotations without contradiction.

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 informative but slightly verbose. It front-loads the key purpose and adds useful details like determinism and no legal advice. Could be trimmed slightly, but still efficient for the information provided.

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 provides sufficient context: each entry has verbatim text and source URL, undated entries are meta.undated, and it notes the tool returns JSON. This is complete for a calendar tool with no required parameters.

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

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

Schema coverage is 100%, so baseline 3. The description adds meaning by explaining the 'all' alias for past dates and referencing list_jurisdictions for the jurisdiction parameter. This provides helpful guidance 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 returns 'upcoming AI-law effective/compliance-date calendar' derived deterministically from a curated corpus. It specifies the tool returns JSON with verbatim deadline text and source URL, distinguishing it from siblings like list_ai_laws which list laws, not deadlines.

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 mentions it's free at every tier and provides an alternative subscription method (iCal feed). It notes undated entries are reported as meta.undated. However, it does not explicitly say when to use this tool vs siblings like get_ai_law or list_ai_laws, though context implies it's for deadlines specifically.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds data source attribution (AI Law Tracker, CC BY 4.0) and legal disclaimer, providing value 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, front-loaded with purpose, followed by data source and disclaimer. 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?

For a simple fetch tool with 1 parameter, full annotations, and no output schema, the description covers purpose, data source, and legal disclaimer. Complete 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%, baseline 3. Description adds context: 'the record uuid (the `id` from a list/search result).' This clarifies the expected value beyond the schema's description.

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 fetches a single AI-law record by stable id, and explicitly distinguishes from siblings by specifying the id source ('the id field from search_ai_laws / list_ai_laws'). Verb 'fetch' with specific resource and scope.

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 usage context: use the id from search/list results. Includes disclaimer that it's informational only, not legal advice. Does not explicitly state when not to use or alternative tools, but the sibling tools provide context.

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

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

The description adds useful behavioral context beyond the annotations: it discloses tier restrictions (Pro+ feature with preview for lower tiers) and includes a disclaimer that it is not legal advice. This provides important caveats for the agent's decision-making. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at three sentences, with the key purpose front-loaded. It avoids repetition. Minor verbosity in 'grounded in a real law record + primary source (no LLM)' is acceptable for clarity.

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 description adequately explains what the tool does and its constraints, but lacks detail about the output format or structure, which is important since there is no output schema. It does not mention pagination or limits, which could be relevant given openWorldHint=true.

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

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

Schema coverage is 100%, so the description does not add significant parameter-level semantics. The only additional guidance is 'Provide at least a jurisdiction or a sector,' which is a usage rule rather than parameter 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 defines the tool as providing an interpretation of 'what must I do' regarding AI law obligations, including who, when, and penalties. It distinguishes itself from siblings like get_ai_deadlines and get_ai_penalties by being the broader obligation layer. It also emphasizes grounding in real law records, not LLM output.

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 states that at least a jurisdiction or sector must be provided, and that it's a paid feature with a preview for lower tiers. It also includes a 'NOT legal advice' disclaimer. However, it does not explicitly say when to use this tool over specific alternatives like get_ai_deadlines or get_ai_penalties, though the sibling list implies the distinctions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral context: it is a dataset (not real-time), not legal advice, and includes a sector-level summary. 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?

Three sentences, each essential: content overview, usage constraint, disclaimer. Front-loaded with the core payload description. 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?

The description fully covers what the tool returns (fields + summary), how to invoke it (at least one param), access restrictions, and a legal disclaimer. No output schema exists, but the description adequately describes the response.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by requiring at least one parameter ('Provide at least a jurisdiction or a sector'), which clarifies optionality 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 returns a 'penalty & enforcement dataset' with specific fields (monetary/other penalty, enforcing body, etc.), distinguishing it from siblings like get_ai_obligations. The verb 'get' combined with the resource 'penalties' is 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 instruction 'Provide at least a jurisdiction or a sector' gives clear usage guidance. It also notes the paid feature preview limitation. However, it does not explicitly contrast with sibling tools (e.g., when to use this vs get_ai_obligations).

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 idempotentHint=true. Description adds concrete output details ('liveness + backend status'), going 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?

Single sentence with key information front-loaded. Every word earns its place; no unnecessary 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 parameterless, output-schemaless status tool, the description fully covers what the tool does. No gaps.

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 schema coverage is 100%. Baseline is 4 for zero parameters; description adds no extra parameter info but none is 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?

Description clearly states 'Liveness + backend status of the AI Law Tracker data API' with a specific verb and resource, and easily distinguishes from sibling tools that focus on laws, bills, etc.

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 is self-evident; it's for checking API status. No explicit when-not or alternatives are needed due to its simplicity, but they are absent.

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 read-only, idempotent, non-destructive, and open world. Description adds context about sourcing from primary registry (no LLM) and handling of missing bindings, going 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?

Three-sentence description efficiently front-loads purpose, details, and usage notes with no extraneous 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 one-parameter tool with no output schema, the description covers functionality, usage guidance, behavioral nuances, data source, and legal disclaimer, making it 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?

Input schema covers slug with a brief description. The tool description enriches by explaining the slug's purpose and directing to list_bills for valid values, adding value beyond 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 retrieves one curated landmark bill by slug, listing its core fields, tiered source URLs, citations, and cross-links. It distinguishes from siblings like list_bills and get_law_history.

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 directs to use list_bills for valid slugs and mentions get_law_history for change timeline. Clarifies that missing live records omit history links without fabrication, guiding proper usage.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context beyond annotations: 'Free at every tier. No fabrication. Data by AI Law Tracker (CC BY 4.0). Informational only — not legal advice.' This clarifies data licensing and limitations, complementing the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no wasted words. The first sentence front-loads the core functionality; the second adds licensing and disclaimers. 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?

For a simple one-parameter, read-only tool with rich annotations, the description fully covers what the tool returns, the data source, and important disclaimers (no fabrication, not legal advice). No gaps remain.

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

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

The input schema describes the single parameter 'id' as 'The record uuid (the `id` from a list/search result).' The tool description adds no additional parameter meaning. Schema coverage is 100%, so 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 what the tool does: it retrieves pre-computed citation metadata (identifier, title, alternate names, suggested citation) for a single law record. It distinguishes from siblings like get_law (which gets the law itself) and other info 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 does not explicitly state when to use this tool versus alternatives. It is implied that it is for citation projection, but no direct comparison or exclusion criteria are 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?

Adds beyond annotations: snapshots ascending, 403 behavior, data license, and disclaimer. No contradictions with annotations which already declare safe read-only.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences efficiently covering output, access limits, source, and legal disclaimer. No redundancy.

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

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

Given no output schema, description effectively explains output. Could detail format (array of snapshots) but sufficient. Annotations cover safety.

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?

Single parameter 'id' with schema description 'The record uuid.' Schema coverage is 100%, so description adds little extra. 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?

Clearly states it returns full change history for one law record with ascending snapshots including changes and values. Distinct from siblings like get_ai_law which gets current state.

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 notes it's a paid feature and that anon/free users get a 403. Info-only disclaimer. Does not explicitly compare to siblings but purpose is clear.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds value by stating no URL fabrication, data source (AI Law Tracker, CC BY 4.0), and informational-only disclaimers, enhancing transparency 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?

The description is moderately concise, with each sentence adding useful information (tiers, data provenance, no fabrication, upgrade hint). It could be slightly shortened but remains 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 tool's simplicity (one required param, no output schema), the description adequately explains what is returned: tiered sources with an upgrade hint. It covers the key behavioral aspects sufficiently.

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 only parameter 'id' is fully described in the schema (schema coverage 100%). The description does not add extra semantic meaning beyond what the schema provides, 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 the tool retrieves primary/secondary source URLs for a single law record. It distinguishes itself from sibling tools like get_ai_law and get_law_citations by specifying 'source links' and 'curated jurisdiction sources'.

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 on when to use this tool (to get source URLs) and includes tier-based access details (basic vs Developer+), plus an upgrade hint when sources are withheld. It could mention specific sibling alternatives like search_ai_laws but is sufficient for 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 indicate safe read operation. Description adds context about tiered access, preview vs. full data, and grounding in real laws. No contradiction found.

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?

Concise yet informative; every sentence adds value. Could be slightly improved by structuring the upsell part more compactly.

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 return structure (overview fields, derived obligations/penalties, preview for under-tier). Sufficient for a single-parameter 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% and already explains the 'sector' parameter well (slug from list_sectors). Description merely repeats this guidance, adding marginal value.

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 that the tool returns a sector's cross-section including free overview and derived obligations/penalties. It uses specific language and distinguishes from sibling tools like list_sectors.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to use list_sectors for valid slugs, describes tier-based access and upsell behavior, and clarifies what different tiers receive.

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?

Adds key behavioral details: never returns full article text, data source (AI Law Tracker, CC BY 4.0), informational only disclaimer. Aligns with annotations (readOnlyHint, etc.).

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 well-structured sentences covering returned fields, access tiers, licensing, and disclaimer. No wordiness, front-loaded with core action.

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

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

Provides sufficient context for a news listing tool with 8 parameters and no output schema. Covers access limitations, data source, and disclaimer. Lacks detail on pagination or query syntax, but schema partially covers.

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 individual parameter descriptions. The tool description adds overall context but does not enhance individual parameter understanding beyond 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?

Description clearly states it lists audited AI-regulation news headlines with specific fields (headline, excerpt, publisher, source URL) and distinguishes from sibling tools like list_ai_laws or get_ai_law by focusing on news feed.

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?

Describes access tiers (free/anon vs Developer+) and the data scope, but does not explicitly guide when to use this tool over siblings like list_law_feed or search_ai_laws.

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

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

Annotations already cover readOnlyHint, idempotentHint, openWorldHint, destructiveHint. Description adds data source (AI Law Tracker, CC BY 4.0) and disclaimer ('Informational only — not legal advice'), which are valuable behavioral traits 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. First sentence covers purpose and available filters. Second sentence provides usage guidance, data source, and disclaimer. 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 10 fully documented parameters, rich annotations, clear description of purpose and usage, and no output schema requirement typical for list operations, the description is complete and informative.

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 each parameter described in JSON Schema. Description lists filters but does not add meaning beyond what's already in the schema, meeting baseline expectation.

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 the tool lists AI-regulation records with filters, sorting, and pagination. It uses specific verb 'List' and resource 'AI-regulation records', and explicitly distinguishes from sibling tool search_ai_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 explicit guidance on when to use this tool ('browse laws for a place') and when to use the alternative search_ai_laws ('free-text search'). No ambiguity.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds context about the data source ('primary-sourced landmark registry'), licensing ('CC BY 4.0'), and disclaimers ('not legal advice'), which enhance transparency without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise, using two sentences (effectively one with a dash) to convey purpose, data source, licensing, and disclaimers. It front-loads the key action and is free of unnecessary 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 tool with no parameters and no output schema, the description provides sufficient context: it lists landmark bills, explains their use for get_bill, specifies the data source, licensing, and legal disclaimer. Combined with thorough annotations, it fully equips an AI agent to select and invoke this 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 tool has zero parameters, and baseline is 4. The description adds meaning beyond the empty schema by explaining the output contains 'named, name-searchable AI laws' with 'valid slugs for get_bill,' giving the agent context on what the result will contain.

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 curated landmark AI bills, specifying they are 'the named, name-searchable AI laws (valid slugs for get_bill).' This distinguishes it from sibling tools like list_ai_laws, which likely lists all AI laws, by emphasizing the 'landmark' subset and direct usefulness for get_bill.

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 retrieving valid slugs for get_bill but does not explicitly compare to alternatives like list_ai_laws or state when not to use this tool. It mentions the data is 'free at every tier' and 'informational only,' but lacks explicit guidance on choosing between sibling tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds useful context: the data source (AI Law Tracker, CC BY 4.0) and that it is informational only, not legal advice. 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 short sentences that convey the tool's purpose and a quick note about the data. No unnecessary words, front-loaded efficiently.

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 output schema, the description adequately explains the return value (distribution of record_type and scope). With zero parameters, it is complete for a simple listing tool, though adding a note about typical use cases would improve it.

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 in the schema, so baseline is 4. The description adds meaning beyond the schema by explaining the output (distribution of record_type and scope) and the nature of the data.

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 specifies the verb 'list' and resource 'dataset categories', and elaborates that it returns the distribution of record_type and scope across all records. This distinguishes it from sibling tools that deal with specific laws, bills, or compliance data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidelines on when to use this tool versus alternatives. The openWorldHint and readOnlyHint imply it is safe for exploration, but there is no mention of specific conditions or 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 indicate readOnlyHint, idempotentHint, etc. The description adds value with informational disclaimer and data source attribution, but does not disclose further behavioral traits like rate limits or response format.

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?

Extremely concise: two sentences with no waste. First sentence is front-loaded with purpose, second adds context. Every word earns its place.

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

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

Given no parameters and no output schema, the description is fairly complete: it describes output (countries with record counts), data source, and disclaimer. It could mention sibling differentiation more explicitly, but still adequate.

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 baseline 4 applies. The description appropriately omits parameter details as there are none.

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 national (global-scope) jurisdictions with AI-law record counts, using specific verb 'list' and resource. It distinguishes from siblings like list_jurisdictions and list_us_states by specifying 'national' and including record counts.

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 use (getting an overview of countries with AI laws) but lacks explicit when-to-use or when-not-to-use guidance, and does not mention alternatives among the many sibling tools.

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

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds value by stating data source (AI Law Tracker, CC BY 4.0) and legal disclaimer, as well as noting record counts in output. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose and result, followed by usage hint and disclaimer. No superfluous 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?

With one optional parameter, no output schema, and strong annotations, the description fully covers what the tool does, what it returns, how outputs are used, and important caveats. Complete 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?

Schema coverage is 100% with descriptions. Description adds semantic mapping by listing types (US states, US federal, EU, countries) corresponding to enum values, enriching meaning beyond 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?

Description states specific verb 'list' and resource 'jurisdictions', enumerates covered types (US states, US federal, EU, countries), and distinguishes from siblings like list_us_states and list_countries by being comprehensive. Also clarifies the utility of returned slugs.

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 says to use slugs for jurisdiction argument of other tools, but lacks explicit guidance on when to choose this tool over similar siblings (e.g., list_us_states, list_countries). Does not mention exclusions or prerequisites.

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

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

The description adds valuable context beyond the annotations: it notes the paid access requirement, data source and license (AI Law Tracker, CC BY 4.0), and the disclaimer that it is not legal advice. It also specifies the feed excludes value diffs. This enriches the understanding of behavior without contradicting the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, consisting of two short sentences that front-load the core purpose and then add essential context (paid feature, data source, disclaimer). Every sentence earns its place with no unnecessary 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 description covers the purpose, access restrictions, data source, and legal disclaimer. However, it does not describe the structure of the returned data (e.g., fields in the stream), and there is no output schema to compensate. For a feed tool, this is a notable gap, though the 'human-scannable' phrasing implies a simple format.

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

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

The input schema has 100% coverage with descriptions for all four parameters. The tool description does not repeat or add any additional parameter semantics beyond what the schema already provides, so it meets the baseline but does not exceed.

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 the newest significant AI-law changes, specifying it includes inserts and real field changes but no value diffs. The verb 'list' and resource 'law feed' are explicit, and the scope is well-defined, distinguishing it from sibling tools like list_recent_changes.

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 mentions it is a paid feature (Developer+) and is informational only, but it does not explicitly provide when to use this tool versus alternatives like list_recent_changes. The phrase 'significant AI-law changes' implies a usage context, but no direct comparison or exclusion criteria are given.

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, etc. Description adds tier-based lookback clamping, paid restriction, and data attribution. No contradictions, but does not detail error handling or exact clamping 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?

Two sentences, front-loaded with purpose and usage pattern, then essential caveats. Every sentence earns its place.

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

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

Given 8 parameters and no output schema, description explains core intent and polling mechanism well. Lacks explicit response structure but implies cursor in meta. Sufficient for a list endpoint.

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 covers 100% of parameters with descriptions. Description reinforces 'since' usage and adds tier context for 'limit', but adds little beyond 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?

Description clearly states it's a unified changelog for time-ordered inserts/updates across the dataset, with polling using 'since' cursor. Distinguishes from siblings by focusing on change tracking rather than static lists.

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 describes polling pattern with 'since' parameter and mentions paid feature and tier clamping. Does not provide explicit when-not or alternatives to sibling tools, but usage context is clear.

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

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

Annotations provide readOnlyHint, openWorldHint, etc. Description adds value by stating data source (AI Law Tracker, CC BY 4.0) and that it is informational, not legal advice. No contradiction.

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 fluff. Purpose and usage are immediately front-loaded. Every sentence 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 parameterless list tool with no output schema, description fully explains return format, data source, and integration with other tools. No gaps.

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; schema coverage is 100%. Description adds meaning about return content (slugs with metadata), which is helpful beyond empty schema. Baseline 3, but extra context justifies 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?

Description clearly states the tool lists sector slugs with name, inherent risk, and description, and that it provides valid values for other tools. It distinguishes from sibling tools like get_sector_detail by referencing the index nature.

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 usage context: provides valid sector slugs for get_ai_obligations, get_ai_penalties, and assess_ai_compliance. Does not explicitly exclude other uses but context is clear.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds that the data is 'informational only — not legal advice', which provides a behavioral caveat. However, it does not disclose any additional behavioral traits beyond what annotations already imply.

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, no wasted words. It clearly states the purpose, source, and disclaimer. Every sentence contributes meaning.

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 has no parameters, no output schema, and annotations already cover the behavioral aspects, the description is complete. It explains what the tool returns (US states with record counts) and includes necessary context (source and disclaimer). No missing information.

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 no parameters (0 params, 0 required, schema coverage 100%). The description does not need to add parameter meaning since there are none. Baseline for 0 params is 4, and the description appropriately omits parameter details.

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 'US state jurisdictions with AI-law record counts', specifying both the resource (list of US states) and the associated data (record counts). This is a specific verb+resource combination, and no sibling tool has a similar name, so it distinguishes itself well.

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 does not provide any guidance on when to use this tool versus alternatives. It only mentions the data source and a disclaimer. Among siblings like 'list_countries' and 'list_jurisdictions', it would be helpful to note when to choose this tool over those, but no such guidance is given.

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 safe read-only behavior. The description adds detail: full-text search over a specific dataset, fields returned, data source, and disclaimer. 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 (four sentences) and front-loaded. Each sentence adds value, though it could be slightly more compact. 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 10 parameters and no output schema, the description adequately explains the tool's purpose and return fields. It could mention pagination or rate limits explicitly, but the parameter descriptions cover limits.

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

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

Schema coverage is 100%, so the description does not need to repeat parameter details. The description provides context on search scope but adds limited 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 it is a full-text search over an AI-regulation dataset, specifies the scope (US state+federal, EU, global), and what is returned (law records with jurisdiction, status, URL). This distinguishes it from siblings like list_ai_laws which likely lists without search.

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 when to use (full-text search) but does not explicitly differentiate from alternatives like list_ai_laws or provide 'when not to use' guidance. However, the verb 'search' and scope imply the usage context.

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

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

The description discloses the checkout flow (returns URL, payment process, automatic upgrade) and notes it does not modify data on call, which adds context beyond annotations. Annotations indicate a mutation, so the description helps understand the non-destructive nature.

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?

Description is two sentences, front-loaded with the action and key details. No unnecessary words, every sentence serves a purpose.

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 tool with no output schema, the description sufficiently explains purpose, behavior, and outcome. Minor gap: does not detail the format of checkout_url or error handling, but overall complete.

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

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

Schema coverage is 100%, but the description adds value by including plan prices and optional email pre-fill context, which is not in the schema. This helps the agent understand the business logic.

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 creates a hosted checkout link to upgrade to a paid API plan, listing the specific tiers and prices. This differentiates it from siblings like 'create_free_key' or 'get_bill'.

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 use for upgrading to a paid plan by listing the tiers and prices, but does not explicitly state when to use or not use this tool versus alternatives. The context is clear but lacks explicit exclusions.

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