TwinEthos Lite

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

Annotations already declare readOnly, idempotent and non-destructive, so the burden is lighter; the description still adds real context: dates can change, results are capped at 10 with a `total` count, and the response includes citation, official link and guard. The only soft spot is the unexplained term 'guard', which an agent may not know is a field value versus a prose sentence.

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?

Front-loads the core scope, then when-to-use, then parameters, then a limits disclaimer — a clean structure with little waste. The opening clause ('with citation, official link and the guard in one sentence') is slightly tangled and the phrase 'the guard' reads awkwardly.

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 does well to preview the return fields and total count, and it flags the informational-not-legal-advice caveat. It is still incomplete for a four-parameter filtering tool, since the features and jurisdictions filters are wholly undocumented.

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 0%, so the description carries the full load, yet it only explains `within_days` (default 365, max 730) and implicitly the 10-result cap. `features` and `jurisdictions` are never mentioned, leaving two of four parameters with no semantics anywhere.

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?

Specific verb-and-resource framing: binding AI-law obligations starting soon, sorted soonest first, with citation, official link and guard. The forward-looking framing cleanly separates it from sibling recently_in_force (past-facing) and search_laws (general lookup).

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?

Gives concrete triggering questions ('what is coming', 'what to prepare for', 'which AI laws take effect next year'), which is strong when-to-use guidance. It stops short of naming the alternatives (recently_in_force, search_laws) and stating when not to use it, so it is a 4 rather than a 5.

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

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

Beyond the read-only and idempotent annotations, it discloses that the requirements are TwinEthos's summary rather than official text and that the information is not legal advice. These provenance and reliance limitations are meaningful behavioral context for interpreting and presenting the result.

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 front-loads the tool's purpose and output contents, then gives invocation guidance and limitations in two short labeled sections. Despite the dense opening sentence, there is no repetitive or expendable wording.

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 single-input read operation without an output schema, the description thoroughly supplies the expected response contents, source character, workflow, and legal-use limitation. The residual ambiguity around rule_id provenance or format is minor relative to the otherwise complete invocation guidance.

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 0% schema description coverage, the description partially compensates by referring to a named rule ID and suggesting a workflow that follows search_laws. However, it does not explicitly document how to obtain the identifier from search results or provide any expected format or example.

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 explains one rule in plain terms and enumerates the resulting requirements, applicability, exclusions, dates, citations, links, and suggested guard. Its workflow reference to search_laws also distinguishes this single-rule explanation from law discovery.

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

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

It explicitly says to use the tool after search_laws or when someone names a rule ID. The statement that it never returns official text supplies a clear when-not boundary, although it does not name an alternative tool for obtaining that text.

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, closed-world behavior, so the lower bound is met. The description adds real context beyond them: the result is capped at 5 guards, the per-guard content is specified, and it explicitly disclaims that output is informational rather than legal advice, which matters for how an agent should relay results. It does not discuss ranking ties or what happens with no matching features/markets.

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 text is short, front-loaded with the output description, then usage, then limits in labeled segments. The first sentence is dense and slightly convoluted ('ranked: each guard in one sentence, how many rules...'), but no sentence is filler. Minor tightening would help.

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 and 0% schema coverage, the description usefully explains the return payload and the 5-item cap. But it omits default behavior when features or jurisdictions are null, which is significant for a planning tool where omitting filters presumably broadens the result. Adequate but leaves a functional gap.

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

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

Schema description coverage is 0% for three parameters, so the description must carry the meaning. It partially does: 'these AI features and markets' maps to the features and jurisdictions arrays, and 'Returns at most 5 guards' corresponds to limit's default of 5. However, it never states the expected value format for features/jurisdictions (names, codes, slugs) or what happens when they are omitted, which is the main ambiguity an agent faces.

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 states a concrete output: a ranked shortlist of 'guards' that address the most rules for given AI features and markets, with per-lane rule counts and binding-law locations. The verb ('plan') is implicit in the ranking framing, and the term 'guard' is domain jargon left undefined, but the shape of the result makes the tool's function inferable. It implicitly differs from siblings like explain_rule or search_laws by being a prioritization/planning tool, though it never says so explicitly.

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

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

It gives an explicit 'When to use' clause: someone asks what to build first, or how to cover several markets at once. That is a clear triggering context. It does not name when-not-to-use conditions or point to the sibling tools (explain_rule, search_laws) for the cases this tool does not serve, so routing guidance is incomplete.

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 this as a safe, idempotent, closed-world read, so the bar is lower. The description still adds real behavioral content: a hard return cap of 10, a `total` count, the within_days ceiling of 730, the semantic that dates mark when a duty started applying rather than enactment, the note that later phases of an in-force law are marked as such, and the informational-not-legal-advice limit.

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?

Front-loaded with the payload description, then when-to-use, then the parameter note, then limits — a sensible order with almost no filler. The single dense opening sentence is long but each clause (ordering, citations, link, guard) conveys distinct 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?

With no output schema, the description usefully characterizes what comes back (citation, official link, one-sentence guard, total). However, for a 4-parameter tool at 0% schema coverage, leaving `features` and `jurisdictions` undefined is a material gap an agent would hit on the first filtered call.

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 0%, so the description must carry parameter meaning and it only partly does. within_days gets default, maximum, and semantics; limit is only implied by 'Returns at most 10'; and `features` and `jurisdictions` — two of the four parameters — receive no explanation of accepted values or filter behavior anywhere.

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 first sentence names the resource precisely (binding AI-law obligations that started applying recently) and specifies ordering, attached fields, and the one-sentence guard. An agent can tell this is a retrospective 'what just took effect' tool, which implicitly separates it from coming_into_force, though no sibling is named explicitly.

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?

An explicit 'When to use' clause covers three concrete question shapes: what took effect recently, when the newest AI law in a market began applying, and what changed this year. It gives no exclusions or pointers to siblings such as coming_into_force or search_laws, so routing between overlapping tools is left to inference.

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

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

With readOnlyHint/idempotentHint already covering the safety profile, the description still adds real behavior: a hard cap of 10 results, the relevance ordering ('words matched, then rules specific to the features, then binding law in force'), and the informational-not-legal-advice limit. The only soft spot is that the stated 'at most 10' is not reconciled with the separate `limit` parameter, which could accept other values.

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?

Front-loaded with a one-sentence purpose, then labeled blocks for when-to-use, per-parameter meaning, ranking, and limits — an agent can skim to what it needs. It is dense and slightly long, with a bit of overlap between the when-to-use examples and the feature list, but no sentence is filler.

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

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

No output schema exists, yet the description specifies returned fields (lane, citation, official link, applies-from date, TwinEthos explorer page), the result cap, and the ranking order. Combined with the safety annotations, an agent has everything needed to call it and interpret 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 0%, so the description carries the load and mostly does: jurisdictions are 'market codes (EU, US-CA, US-CO; US matches every state)', features are enumerated patterns (chat, rag, agentic, decision_pipeline, content_generation, classification, recommendation, embeddings), and text is defined as words matched in titles, summaries and tags with examples. The fourth parameter, `limit`, is never explained — and the 'at most 10' phrasing arguably conflicts with it.

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 opens with a specific verb and resource: 'Find the AI-law rules (EU AI Act, US state AI laws, China, Korea...) that apply to an AI feature in a market,' and it names the return unit (cited provision + official link). It also distinguishes itself from date-oriented siblings by stating 'It does not sort by date: use coming_into_force or recently_in_force for dates,' so an agent can route correctly without opening any schema.

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?

An explicit 'When to use' clause supplies concrete triggers ('which laws, regulations or standards apply to what they are building') plus example topics (chatbot disclosure, hiring, credit, deepfakes, minors, content labels, training data). The exclusions/alternatives are equally explicit, pointing date-based questions to coming_into_force and recently_in_force.

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