Horizon AI Intelligence

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

Annotations already declare readOnlyHint=true, and the description adds important methodological context: the corpus is Horizon's, 'invisible' means zero Western pickup within that corpus. This transparency about data scope and limitations goes beyond annotations and helps the agent understand the tool's 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 with zero wasted words. The first sentence explains what it does and how results are organized; the second clarifies the corpus scope. Front-loading the key purpose and then adding necessary caveats is effective.

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 parameters, readOnly annotation, and existence of an output schema, the description is complete. It explains the concept of blind spots, the ranking, per-region counts, and the corpus limitation. No additional context is needed for correct invocation.

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 schema fully describes the parameters days and limit. The description does not add parameter-specific details but provides context about ranking and per-region counts, which are output-related. Baseline 3 is appropriate since no extra parameter semantics are needed.

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

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

The description clearly states it returns coverage blind spots by regional press that Western feeds are missing, specifically for companies, models, and people. It answers a concrete question ('What is Western tech media missing right now?') and distinguishes from sibling tools by focusing on cross-regional blind spots rather than a single region's 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 a clear use case: answering what Western tech media is missing. It implies usage when comparing regional vs. Western coverage. However, it does not explicitly list alternative tools or when not to use this tool, though the sibling list and context provide some 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?

No annotations are present, so the description carries the burden. It mentions return fields (titles, sources, links) and ranking by momentum, but does not discuss rate limits, authentication, or whether the operation is read-only. The behavioral context is adequate but not comprehensive.

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 front-loads the core purpose and content, the second specifies return format. No redundant information, efficiently written.

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 simple optional parameter and an output schema (known to exist), the description sufficiently covers input and output. It specifies content type, ranking, and returned fields. A mention of default ordering or more details on 'momentum' could improve completeness, but current level is strong.

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, 'limit', is fully described in the input schema (default 15, cap 40). The description adds no additional parameter meaning, so 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 retrieves a signal for China/East-Asia AI, listing specific entities and types of items (models, labs, analysis). It distinguishes from siblings like get_region_signal and get_topic_signal by its focused geographic and thematic 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?

Implied usage is for China/East-Asia AI content, with a hint that it covers items missed by Western feeds. However, no explicit when-not or comparison to sibling tools is 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?

No annotations are provided, so the description carries full burden. It discloses the sources and that the briefing is aggregate, but does not mention read-only behavior, rate limits, or any side effects. The description is adequate for a presumably non-destructive read operation.

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 zero waste. The first sentence specifies what the tool does and from which sources. The second explains the optional parameter. 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 single-parameter tool with no output schema or annotations, the description covers the purpose, sources, and optional date. It lacks output format details, but given the simplicity, it is largely 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% with a single optional date parameter. The schema already describes the parameter. The description adds that the archive is public, providing marginal extra context. Baseline 3 is appropriate.

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

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

The description clearly states the tool provides an aggregate AI industry briefing covering top stories, signals, entrants, and regulation updates from specific sources (HN/Reddit, legislative). This distinguishes it from sibling tools like get_topic_signal or get_region_signal which focus on narrower 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 mentions the optional date parameter and that the archive is public, but does not explicitly guide when to use this tool versus alternatives. Usage is implied for a general daily briefing but no when-not-to-use or alternative recommendations.

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 no annotations, the description carries full burden. It reveals incremental polling behavior, ordering, pagination via cursor, and that bodies/higher limits are paid. Sufficiently transparent.

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

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

Two sentences, front-loaded with core purpose and behavior. Every sentence adds unique value. No fluff.

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 having an output schema (balance), description explains the cursor mechanism, ordering, and content restrictions. Could mention error handling for invalid cursors, but overall complete for a simple polling 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% (baseline 3). Description adds meaning to the cursor parameter (opaque, from previous call, omit on first) and notes limit cap and paid tier. Adds 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?

Description clearly states it returns AI news items added since a cursor, with oldest-to-newest ordering, and a nextCursor for continuation. This distinguishes it from siblings like search_news or get_daily_briefing.

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 guidance on first call (omit cursor) vs subsequent calls (use nextCursor). Also notes content limitations. However, it does not explicitly compare to sibling tools or state when not to use.

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 no annotations provided, the description carries full burden for behavioral disclosure. It mentions the data is 'recent, relevance-scored' but does not disclose whether the operation is read-only, idempotent, or if there are any side effects. Key behavioral traits like caching, rate limits, or data freshness are missing.

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

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

The description is two sentences long with no filler. The first sentence defines the tool's purpose, and the second sentence provides a sibling comparison. Every sentence adds value, and the structure 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?

For a simple tool with two parameters and an output schema, the description covers the essentials: what the tool returns and how it relates to a sibling. It does not explain the output structure, but the presence of an output schema makes that unnecessary. It could briefly mention that region values come from the schema enum.

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% (both parameters have descriptions). The description adds context about the signal contents but does not provide additional parameter-specific meaning beyond what the schema already offers. 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 it returns a region-specific AI signal with recent, relevance-scored items ranked by momentum. It explicitly distinguishes from the sibling tool get_china_signal by noting it works for any supported region, making the purpose 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?

The description indicates to use this tool for region-specific signals and contrasts it with get_china_signal for China-specific use. It does not explicitly exclude other siblings like get_topic_signal, but the context is sufficiently clear for when to use this tool.

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?

No annotations provided, so description must fully disclose behavior. It states the output (headlines with stage) but omits details on data freshness, pagination, rate limits, or read-only 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?

Single sentence that is compact, front-loaded with core functionality, and contains no redundant 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 simplicity (2 optional params, no output schema), the description explains purpose and data returned adequately. Lacks details on output format but sufficient for basic understanding.

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 has 100% coverage with clear descriptions for limit and jurisdiction. The tool description adds no extra 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?

Description clearly specifies the resource (AI legislation headlines), jurisdictions (US Congress, UK, EU), and the legislative stage information. It distinguishes from siblings like get_daily_briefing (broader) and search_news (generic).

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 guidance on when to use this tool versus alternatives. Does not provide context for prerequisites, limitations, 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?

No annotations provided, so description must disclose behavioral traits. It describes the operation as returning similar items via embedding similarity, which implies a read-only behavior. Could benefit from mentioning rate limits or data freshness but is adequate.

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 efficiently conveys purpose, method, input sources, and output. No wasted words; ideally front-loaded with key 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 output schema exists, return values need not be detailed. Description covers input source, similarity method, and output fields. With only two non-nested parameters and low complexity, it is sufficiently complete.

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

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

Input schema covers both parameters (id, limit) with descriptions. The description adds context for the limit parameter (default 10, cap 25) and explains the id's source, providing value 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?

Description clearly states 'Semantic more like this' and explains the tool returns similar recent items by embedding similarity given an item ID, distinguishing it from siblings 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?

Explicitly states the input ID comes from get_new_since, search_news, or the feed, providing clear context for when to use this tool. Lacks explicit when-not-to-use instructions but context is sufficient.

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?

No annotations provided, so description carries full burden. It discloses that items are 'recent, relevance-scored' and 'ranked by momentum', but lacks detail on auth needs, rate limits, or any destructive aspects.

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 wasted words. Front-loaded with core purpose, then useful comparison.

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 low complexity, presence of output schema, and 100% parameter coverage, the description is complete. It covers purpose, relationship to sibling, and parameter hints.

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 both parameters are well-documented. Description adds no extra meaning beyond what schema provides, which is sufficient for this simple tool.

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

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

Description clearly states it gets the current AI signal for a topic, with recent relevance-scored items ranked by momentum. It explicitly distinguishes from sibling tool get_region_signal by noting 'sliced by subject instead of geography'.

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?

Description gives clear when-to-use context by comparing to get_region_signal, and mentions default limit and cap. It does not explicitly state when not to use, but the comparison provides 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates the tool is a read-only information source that describes the corpus, but lacks details on output format, scope (e.g., whether it returns a list or narrative), or any potential side effects. The description is adequate but not rich in 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?

The description is two sentences long, with no redundant wording. The first sentence succinctly states the tool's coverage, and the second provides usage guidance. Every sentence adds value, making it highly 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 has no parameters, no output schema, and no annotations, the description provides sufficient context for understanding its purpose and usage. It covers what the tool describes (sources, filter values, regions) and when to use it. However, it could slightly improve by hinting at the output structure.

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 zero parameters, so the description does not need to add parameter-level details. Baseline for no parameters is 4. The description does not introduce unnecessary parameter information, which 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 explicitly states the tool's function: to describe the Horizon corpus, including ingestion sources with their filter values, regional lenses, and filtering methods. It uses specific verbs ('Describe,' 'Call this to understand') and clearly distinguishes from sibling tools that perform specific queries (e.g., get_china_signal, search_news) by positioning this as a meta-tool for preliminary understanding.

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: 'Call this to understand the corpus before querying.' It implies the optimal usage scenario (before other queries) and implicitly guides the agent to use this tool first. However, it does not explicitly state when not to use it or list alternatives, which would strengthen the 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?

No annotations are provided, so the description carries the full burden. It discloses return limit (top 3) and result type (titles with links), and mentions subscription requirement for full search. However, it does not state whether it is read-only, authentication needs, 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 sentences, concise and front-loaded with the core purpose and result format. Every word adds value with no waste.

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 (1 param, output schema exists), the description adequately covers scope (recent AI news, top 3 results) and limitation (full-text requires subscription). It does not specify time range, but this is acceptable for a fast search 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?

The input schema has 100% coverage for its single parameter 'query' with a clear description. The description adds no further semantics beyond 'search terms', which matches the schema. Baseline 3 is appropriate.

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

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

The description clearly states it performs a 'quick teaser search over recent AI news' returning 'top 3 matching titles with links'. This is a specific verb+resource, and it distinguishes from full-text search which requires a subscription, differentiating it from siblings.

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 indicates when to use this tool ('quick teaser search') and when not to use it ('Full-text search... requires a Horizon subscription'). It provides clear context but does not name a specific alternative sibling tool.

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