Horizon AI Intelligence

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

The readOnlyHint annotation already covers safety, and the description adds valuable scope context by clarifying that 'invisible' means zero pickup only in Horizon's tracked Western feeds, not a claim about all media. This prevents misinterpretation of 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?

The description is two concise sentences that front-load the core purpose and then add essential scope clarification. No unnecessary words or repetition; 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?

The tool has an output schema, so return values are already documented. The description covers the core concept, the question it answers, and the important measurement scope, making it complete for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, with clear descriptions for 'days' and 'limit', so the baseline of 3 applies. The description does not add additional parameter-specific context, but none is needed given the schema's clarity.

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 exactly what the tool does: identifies companies, models, and people covered by regional press but not by Western feeds, ranked with per-region counts. This clearly distinguishes it from sibling tools like get_region_signal or get_topic_signal by focusing on gaps in Western coverage.

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 a clear use case by answering the question 'what is Western tech media missing right now?', giving strong contextual guidance. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of 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?

The annotation readOnlyHint already signals safety. The description adds useful context about the returned signal (ranked by momentum) and its unique coverage of Western-feed blind spots. No hidden behaviors are disclosed, but the output schema covers return format, so this is sufficient.

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: the first defines the tool's purpose and scope, the second gives actionable alternative guidance. 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?

For a single-parameter read-only tool with output schema, the description covers purpose, scope, ordering, and when to use the alternative. It is fully sufficient for correct invocation without further external context.

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

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

The input schema fully documents the 'limit' parameter with default and cap (100% coverage). The description does not add extra parameter semantics, but per the rubric, baseline 3 is appropriate when schema covers all 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?

The description clearly identifies the tool as a shortcut for 'get_region_signal' with region 'china', specifying exact geographic scope and example model providers (Qwen, DeepSeek, Zhipu, Moonshot, ModelScope). It also mentions the ranking by momentum, which distinguishes it from broader sibling 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?

Explicit guidance is provided: 'Prefer get_region_signal if you may query more than one region.' This gives a clear condition for when to choose an alternative, making the usage context unambiguous.

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

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

Annotations already declare readOnlyHint=true, so the read-only safety profile is known. The description adds valuable behavioral context: it is a curated once-daily synthesis (not raw), sources are explicitly named (HN/Reddit and official legislative), and the archive is public with any past date accessible. This goes beyond the annotation by clarifying what the tool returns and its access assumptions, though it stops short of describing response structure or rate limits.

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 three concise sentences: the first establishes the core purpose, the second differentiates from a sibling, and the third explains the optional parameter. It is front-loaded with the most important information and every sentence serves a distinct purpose with no filler.

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

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

For a tool with one optional parameter, a read-only annotation, and no output schema, the description is complete. It explains what the tool does, when to use it (vs the sibling), and exactly how to use the parameter. It even adds transparency about sources and archive accessibility, leaving no significant gaps for an agent to invoke it 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?

Schema coverage is 100%, with the parameter fully described in the schema ('Optional YYYY-MM-DD for a past briefing... omit for the latest briefing'). The description only repeats the same semantics with slightly different wording, adding no new parameter-specific detail beyond the schema. Thus, the baseline of 3 applies.

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

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

The description opens with a specific verb+resource: 'Today's aggregate AI industry briefing' and enumerates contents (top stories, emerging signals, new entrants, regulation updates). It explicitly contrasts with get_new_since ('for raw item-level updates since your last check, use get_new_since instead'), clearly distinguishing its aggregated, curated scope from the raw item-level 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?

The description provides explicit when-to-use guidance by naming the alternative get_new_since for raw updates, and gives condition for using the optional date parameter ('Optionally pass any past date...'). It also implies the default use case (latest briefing) by omission, aligning with the schema's 'omit for the latest briefing'.

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 readOnlyHint annotation, the description discloses ordering (oldest→newest), pagination via nextCursor, the omitted-cursor behavior for starting fresh, content returned (titles, links, topics), and paid-tier restrictions. This is substantial contextual disclosure.

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, front-loaded with the core action, and every clause provides value. No fluff or redundancy; it achieves high density of useful 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?

Given the tool's small parameter surface and the presence of an output schema, the description adequately covers return contents, pagination mechanics, and usage patterns. There are no significant gaps in what an agent needs to know to invoke it 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 input schema covers 100% of parameter descriptions, so the baseline is 3. The description adds meaningful guidance by explaining the cursor's rollover ('nextCursor for your next call') and clarifying the omit-cursor first-call scenario, earning 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 explicitly states 'Incremental poll: raw item-level AI news added since a cursor' with a clear verb and resource, and distinguishes itself from get_daily_briefing. It is specific and unambiguous.

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

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

It provides an explicit use case ('what's new since I last checked') and names the alternative tool for curated synthesis (get_daily_briefing). It also explains the behavior when omitting the cursor, covering initial and subsequent calls.

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=true, but the description adds substantial behavioral context: items are relevance-scored and ranked by momentum, local-language press is translated to English, and the output includes titles, sources, and links. This goes well beyond the annotation to explain how the tool behaves and what the user receives.

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 three sentences, front-loading the core purpose, then covering translation and return details. Every sentence contributes distinct information with no redundancy or filler.

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

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

With a readOnly hint, a present output schema, and full parameter schema coverage, the description still adds value by explaining the signal's nature (relevance-scored, momentum-ranked), translation of local press, and return fields. It also addresses the relationship to a sibling tool, making it complete for the tool's 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 for both region (enum) and limit (default/cap). The description adds little beyond the schema—only reiterating the region values and noting that get_china_signal is a preset, which doesn't materially enhance parameter meaning. Baseline 3 is appropriate given the high schema coverage.

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

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

The description clearly states the tool retrieves the current AI signal for a region, enumerates the valid regions (china, korea, japan, eu), and describes the content (recent, relevance-scored items ranked by momentum) along with the return format (titles, sources, links). It distinguishes itself from the sibling get_china_signal by positioning itself as the canonical regional tool with get_china_signal as a preset.

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

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

The description explicitly states it is 'the canonical regional tool' and calls out get_china_signal as a preset for region 'china', directly informing the agent when to use this tool versus an alternative. While it doesn't compare against every sibling, the guidance is clear and actionable for regional signal needs.

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, which is not contradicted. The description adds useful context beyond the annotation: it clarifies the geographic coverage and the concept of legislative stage, giving the agent a better sense of the tool's behavior without repeating the safety cue.

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 one sentence, front-loaded with the key term 'Current AI legislation headlines', and includes the legislative stage enumeration. Every word contributes value with 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?

For a simple read-only tool with two optional parameters and no output schema, the description sufficiently conveys what is returned (headlines with legislative stage) and the scope. It is complete enough for an agent to select and invoke the tool correctly.

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

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

Schema description coverage is 100%, with each parameter (limit, jurisdiction) well-documented. The description does not add significant parameter semantics beyond what the schema already provides, so the baseline score of 3 applies.

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

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

The description clearly states the tool's purpose: returning current AI legislation headlines across US, UK, and EU, with legislative stage. It specifies the resource (AI legislation) and scope (three jurisdictions), distinguishing it from sibling tools like get_china_signal or get_topic_signal.

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 (when AI regulation headlines are needed) and implicitly defines scope. It does not explicitly contrast with alternatives or list exclusions, but the jurisdiction list and legislative stage terminology make the intended use obvious.

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

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

With the readOnlyHint annotation covering safety, the description adds meaningful behavioral context by explaining the use of embedding similarity, recency filtering, and the nature of the output. This enriches understanding beyond the annotation alone, 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 a single, front-loaded sentence that packs in the core concept ('Semantic more like this'), input requirements, and output contents without any fluff. Every part 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 the tool's low complexity, existing output schema, readOnly annotation, and a clear description covering input source and expected output, there are no significant gaps. The description sufficiently orients an agent on when and how to invoke 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?

The schema fully documents both parameters (id and limit). The description adds extra meaning by clarifying that the id originates from specific source tools (get_new_since, search_news, or the feed), which complements the schema's generic 'UUID of an item from a previous result'.

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 semantically similar items via 'semantic more like this' given an item ID, and explicitly lists the output fields (titles, sources, similarity scores, links). It distinguishes itself from sibling tools by focusing on item similarity rather than new/search/regional signals.

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 conveys when to use it by specifying that the input is an item ID from get_new_since, search_news, or the feed, implying it is for follow-up exploration after retrieving an item. It does not explicitly name alternatives, but the context and sibling list make the usage scenario 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, and the description adds behavioral context by explaining the items are relevance-scored and ranked by momentum. This goes beyond the annotation without contradicting it, though it does not cover pagination or rate limits.

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 efficient sentences. The first defines the tool's output and ranking, the second provides a sibling comparison. No wasted words, and the most important info is front-loaded.

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

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

With an output schema present and annotations covering safety, the description adequately explains the tool's purpose and ranking logic. It is complete for a straightforward read-only tool, though it could optionally mention the available topic values.

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 parameter descriptions for both 'topic' and 'limit'. The description does not add significant extra meaning beyond referring to topic as 'subject', so the baseline 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 what the tool does: retrieves the current AI signal for a topic, defined as recent, relevance-scored items ranked by momentum. It also differentiates from get_region_signal by noting the subject-based slicing, making the purpose distinct and specific.

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

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

The description explicitly compares the tool to get_region_signal, indicating when to use this over a geographic alternative. This provides clear context, though it does not mention exclusions or other 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?

The readOnlyHint annotation already signals safe read-only behavior. The description adds context about what the tool covers (sources, filters, regional lenses) but does not disclose deeper behavioral aspects like return format, pagination, or edge cases. This meets the minimum bar but does not go beyond it.

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, front-loaded with the main purpose. The first sentence lists the key contents, and the second provides usage timing. There is zero redundancy and 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?

For a zero-parameter, read-only tool with no output schema, the description fully covers what the tool does and when to use it. It gives a complete mental model for an agent deciding whether to call this before other queries.

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 zero parameters, the schema fully covers parameter concerns. The description adds no param details, but none are needed. Baseline for 0 params is 4, and the description's focus on purpose is appropriate.

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

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

The description clearly states the tool's function with a specific verb ('Describe') and identifies the resource ('Horizon coverage'). It lists the concrete contents (ingestion sources, filter values, regional lenses), which distinguishes it from sibling query tools like search_news or get_topic_signal.

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

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

The description explicitly says to 'Call this to understand the corpus before querying,' giving a clear when-to-use instruction. It does not name specific alternatives or when-not-to-use conditions, but the context of being a pre-query orientation tool is sufficient guidance.

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

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds behavioral specifics: it returns only the top 3 titles with links, and that advanced features are gated behind a subscription. This goes beyond the structured annotations and provides useful context for the agent.

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. The first clearly articulates the tool's core function and output; the second states the limitation and upgrade path. Every phrase contributes meaning, with no redundant or filler 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?

Given the simple input (one string), the presence of an output schema, and the read-only annotation, the description is largely complete. It explains the scope ('recent AI news'), result count (top 3), and the subscription boundary. Minor ambiguities like the exact meaning of 'recent' are not critical.

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 for the single 'query' parameter, describing it as 'Search terms.' The description does not add additional detail about parameter semantics, such as syntax, length restrictions, or examples. With full schema coverage, the baseline 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?

Description states the tool performs a 'quick teaser search over recent AI news' and specifies the output: 'top 3 matching titles with links.' This is a clear verb+resource pair and distinguishes it from the sibling get_* tools, which are not search tools. The teaser scope is explicit.

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 clearly indicates when to use this tool ('Quick teaser search') and contrasts it with the full-text search capability that 'requires a Horizon subscription.' This implies that for full-text, filters, or history, users should not rely on this tool. However, it does not explicitly name an alternative tool, though none of the siblings fill that role.

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