Gate News MCP

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

Annotations already show readOnlyHint, idempotentHint, and destructiveHint, so the tool is safe. The description adds value by detailing return components (summary, events, internal pool, completeness status) and its aggregator role, 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?

Two sentences front-loaded with purpose, then return details and role. 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 output schema exists and the tool is a data aggregator, the description provides sufficient context about inputs and returns. Minor gap: doesn't mention default values for time_range and mode, but schema covers them.

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 descriptions in schema are adequate. The tool description does not add substantive meaning beyond the schema for parameters like query, coin, lang, mode, time_range.

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 uses a specific verb ('explain') and resource ('crypto asset price move in a given time window'), and clearly differentiates from siblings like news_events_get_latest_events by stating it returns a summary, events, and internal items for attribution reasoning.

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 it is a data aggregator and the downstream agent performs final reasoning, implying when to use it. However, it does not explicitly exclude alternatives or provide comparative 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?

The description adds valuable behavioral context beyond annotations: it specifies that only one event_id is processed, explains the error behavior for unknown IDs, and references the source of valid IDs. While annotations cover safety aspects (destructiveHint: true, etc.), the description provides operational details that help the agent understand how to use the tool correctly.

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 with three information-dense sentences that each serve distinct purposes: stating the core function, providing alternative guidance, and describing error behavior. No wasted words, and the most critical information appears first.

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

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

Given the tool's single parameter, comprehensive annotations, and the presence of an output schema, the description provides exactly what's needed: clear purpose, usage guidelines, and behavioral context. The output schema will handle return values, so the description appropriately focuses on when and how to use the 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?

With 100% schema description coverage, the input schema already fully documents the single required parameter. The description reinforces that it's 'for one event_id only' and references 'get_latest_events items' as the source, but doesn't add significant semantic value beyond what's in the schema. This meets the baseline for 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 specific action ('Full detail for one event_id only') and resource ('event'), distinguishing it from sibling tools like 'get_latest_events' for filtered lists or timelines. It uses precise language that goes beyond just restating the name.

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 when to use this tool ('Full detail for one event_id only') and when to use alternatives ('Filtered event list or timeline -> get_latest_events'). It also provides exclusion guidance ('Unknown id returns not found'), giving complete context for proper tool selection.

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 some behavioral context beyond annotations: it mentions that results include 'event_id' and hints at a 'filtered' list. However, annotations already cover key traits (readOnlyHint=false, destructiveHint=true, etc.), and the description doesn't fully disclose implications like what 'destructiveHint: true' means in practice (e.g., data modification risks) or rate limits. With annotations present, the bar is lower, but the description adds only moderate value, warranting a 3.

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 and front-loaded: it uses only two sentences with zero wasted words. The first sentence states the purpose and key output detail, while the second provides clear usage guidelines. Every sentence earns its place, making it highly efficient and well-structured.

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 (7 parameters, annotations, and an output schema), the description is reasonably complete. It clarifies the tool's role vs. siblings and output structure. With an output schema present, it doesn't need to explain return values. However, it could better address behavioral aspects implied by annotations (e.g., destructive nature) to be fully comprehensive, so it scores a 4.

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 description does not add any parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, the baseline score is 3. The description mentions 'filtered' but doesn't elaborate on how parameters like 'coin' or 'event_type' affect filtering, so it doesn't enhance parameter semantics 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's purpose: 'Filtered event list or timeline; each row includes event_id.' It specifies the verb ('Filtered') and resource ('event list or timeline'), and distinguishes from sibling tools by mentioning 'get_event_detail' for details and 'search_news' for headlines. However, it doesn't explicitly differentiate from all siblings like 'news_feed_get_exchange_announcements' or 'news_feed_search_ugc', keeping it at a 4 rather than a 5.

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 vs. alternatives: it directs to 'get_event_detail' for detailed event information and 'search_news' for headline/news feed purposes. This helps the agent choose appropriately. However, it doesn't explicitly state when not to use this tool (e.g., vs. other siblings like 'news_feed_get_social_sentiment') or include prerequisites, so it scores a 4 instead 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 description adds some behavioral context beyond annotations: it clarifies the scope ('venue-published exchange notices') and contrasts with search_news. However, annotations already provide key hints (readOnlyHint=false, destructiveHint=true, openWorldHint=true, idempotentHint=false), so the description doesn't need to repeat these. It doesn't add details like rate limits or authentication needs, but doesn't contradict annotations either.

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 and front-loaded: a single sentence states the purpose and usage guidelines, with zero wasted words. Every element (purpose, scope, alternative tool) earns its place 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 the tool's complexity (8 parameters, annotations covering key behavioral hints, and an output schema), the description is complete enough. It clarifies the tool's niche versus siblings, and with annotations and schema providing detailed parameter and behavioral information, no additional explanation of return values or low-level behavior is needed.

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 description coverage, the input schema already documents all 8 parameters thoroughly. The description doesn't add parameter-specific semantics beyond what's in the schema, but it does reinforce the distinction between 'venue-published exchange notices' (for this tool) and 'general crypto media headlines' (for search_news), which contextualizes parameters like 'query' and 'announcement_type'.

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 with specific verbs ('Read venue-published exchange notices') and resources ('listings, delistings, maintenance'), and explicitly distinguishes it from its sibling tool search_news by contrasting 'venue-published exchange notices' with 'media rumors or general crypto headlines'.

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 guidance on when to use this tool versus alternatives: it specifies 'Venue-published exchange notices' as the proper use case and directs users to 'search_news' for 'Media rumors or general crypto headlines,' clearly differentiating 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?

The description adds some behavioral context beyond annotations, such as specifying the aggregation includes 'sample tweets' and the time range for sentiment aggregation. However, it does not address the annotations' implications (e.g., destructiveHint: true, idempotentHint: false) or provide details on rate limits, authentication needs, or error handling. With annotations covering basic traits, the description adds moderate value but lacks depth in behavioral 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 appropriately sized and front-loaded, starting with the core functionality and then providing usage guidelines. However, it includes some redundancy with the schema (e.g., repeating 'per-coin aggregates') and could be slightly more streamlined, though it remains efficient and informative without unnecessary details.

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 (aggregation with sentiment analysis), rich annotations, and the presence of an output schema, the description is mostly complete. It covers purpose, usage guidelines, and key outputs, but could improve by addressing the behavioral implications of annotations (e.g., destructive nature) or providing more context on limitations. Overall, it is sufficient but not exhaustive.

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

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

The description does not add significant meaning beyond the input schema, which has 100% coverage and already details the parameters (coin and time_range). The description repeats some schema information (e.g., 'per-coin aggregates') but does not provide additional syntax, format, or usage nuances. With high schema coverage, the baseline score of 3 is appropriate as the schema handles most of the parameter documentation.

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 with specific verbs ('Aggregate per-coin social sentiment') and resources ('overall sentiment, positive/negative split, mention count, and sample tweets'), and explicitly distinguishes it from sibling tools ('X/Twitter post search or tweet-level evidence -> search_x. Multi-platform social thread search -> search_ugc'). This provides precise differentiation from alternatives like search_x and search_ugc.

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 guidance on when to use this tool versus alternatives, stating 'X/Twitter post search or tweet-level evidence -> search_x. Multi-platform social thread search -> search_ugc.' This clearly directs users to other tools for specific use cases, making it easy to choose the right tool among siblings.

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=false, openWorldHint=true, idempotentHint=false, and destructiveHint=true. The description adds some behavioral context about the search modes (heat mode vs similarity mode) and what types of content it returns, but doesn't elaborate on the destructive nature or open-world implications beyond what annotations provide.

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 efficiently structured in two sentences: the first states the purpose and scope, the second provides explicit usage guidelines with alternatives. Every sentence earns its place, though the first sentence is somewhat dense with multiple concepts.

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 (13 parameters), the comprehensive 100% schema description coverage, the presence of an output schema, and rich annotations, the description provides complete contextual information. It covers purpose, scope, and usage guidelines adequately for the available structured data.

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 description coverage, the schema already documents all 13 parameters thoroughly. The description adds some high-level context about search modes (heat vs similarity) and platform index scope, but doesn't provide additional parameter semantics beyond what's in the schema descriptions.

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 searches 'the platform news index for headlines, news items, and briefing-style result lists' with specific verbs and resources. It explicitly distinguishes from sibling tools web_search (open-web research) and get_latest_events (event catalog), providing excellent differentiation.

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 guidance on when to use this tool versus alternatives: 'Open-web research with synthesized answers and cited external pages -> web_search. Event catalog with event_id -> get_latest_events.' This gives clear when-not-to-use scenarios with named alternatives.

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 indicate readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=true. The description adds context beyond annotations by specifying the search behavior (vector API vs. OpenSearch based on query presence) and invalid input conditions ('Both empty invalid'), which are not covered by annotations. However, it does not fully explain the implications of destructiveHint=true or openWorldHint=true, leaving some behavioral traits implicit.

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 front-loaded with key information (search behavior and usage guidelines) in a single, dense sentence, followed by exclusions. It is appropriately sized with no wasted words, but the structure could be slightly improved by separating the search logic from the sibling tool distinctions for better readability.

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 complexity (9 parameters, annotations, and an output schema), the description is complete enough. It covers purpose, usage guidelines, and behavioral context, while the output schema handles return values. The annotations provide safety and operational hints, and the description fills in gaps like search modes and exclusions, making it well-rounded for the tool's scope.

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%, so the input schema already documents all 9 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, such as implying the interaction between 'coin' and 'query' for search modes, but does not provide significant additional meaning. With high schema coverage, the baseline score of 3 is appropriate as the description adds some value but not extensive param 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 the tool searches for user-generated content (UGC) from specific platforms (Reddit/Discord/Telegram/YouTube) and distinguishes it from siblings by explicitly naming alternatives: 'X/Twitter narrative -> search_x; headlines -> search_news'. It specifies what it does not cover: 'Not macro economic statistics; not structured event list -> get_latest_events', making the purpose specific and differentiated.

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 guidance on when to use this tool vs. alternatives: 'X/Twitter narrative -> search_x; headlines -> search_news' and exclusions: 'Not macro economic statistics; not structured event list -> get_latest_events'. It also details usage conditions: 'non-empty query uses vector API; coin without query uses OpenSearch. Both empty invalid.', offering clear context for selection.

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 annotations: it clarifies the tool's focus on 'tweet-level evidence and cited posts' and mentions platform fallback behavior. While annotations provide structural hints (readOnlyHint: false, destructiveHint: true, etc.), the description enriches understanding of what the tool actually does. No contradiction with annotations exists.

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 efficiently structured in three sentences: purpose statement, usage guidelines, and sibling tool references. It's front-loaded with the core function and avoids unnecessary elaboration. The only minor inefficiency is the bracketed '[Read]' prefix, which adds little 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?

Given the tool's complexity (19 parameters, annotations, output schema), the description is complete. It clearly states the purpose, provides usage guidelines, and references siblings. With comprehensive schema coverage and an output schema, the description doesn't need to explain parameters or return values, focusing instead on higher-level 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?

With 100% schema description coverage, the schema already documents all 19 parameters thoroughly. The description doesn't add significant parameter-specific details beyond what's in the schema, though it reinforces the primary use of the 'query' parameter. This meets the baseline for 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's purpose: 'Search and analyze X/Twitter discussions for a topic, with tweet-level evidence and cited posts.' It specifies the verb ('search and analyze'), resource ('X/Twitter discussions'), and scope ('tweet-level evidence and cited posts'), and distinguishes it from siblings by explicitly naming alternatives (get_social_sentiment, web_search, search_ugc).

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 guidance on when to use this tool versus alternatives: 'Aggregate social mood, sentiment score, or positive/negative split -> get_social_sentiment. Open-web pages -> web_search. Multi-platform social search -> search_ugc.' This clearly defines the tool's niche and when to choose other 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 provide readOnlyHint=false, destructiveHint=true, openWorldHint=true, and idempotentHint=false. The description adds valuable context about what the tool does (synthesized answers with citations) and what it doesn't do (platform news, Twitter-only). It doesn't contradict annotations but could elaborate more on the 'destructive' aspect (e.g., what gets destroyed).

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 (two sentences) and front-loaded with the core purpose. Every sentence earns its place by providing essential differentiation from sibling tools. No wasted words or 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 tool's complexity (6 parameters, open-world synthesis), the description is complete enough. It clearly states the tool's purpose, distinguishes from siblings, and the output schema exists (so return values needn't be explained). Annotations provide safety/behavioral hints, and schema covers parameters fully.

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%, so parameters are fully documented in the schema. The description doesn't add significant semantic details beyond what's in schema descriptions (e.g., it repeats the query parameter's purpose). Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool's purpose: 'Search the open web and return a synthesized answer with cited external pages.' It distinguishes from siblings by explicitly naming alternatives (search_news for built-in news, search_x for Twitter-only). The verb 'search' and resource 'open web' are 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 provides explicit guidance on when to use this tool vs. alternatives: 'Built-in headline lookup, news-item search, or briefing-style news list -> search_news. X/Twitter-only discussion or tweet evidence -> search_x.' This clearly defines boundaries and sibling tool relationships.

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

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

Annotations already declare readOnly, idempotent, non-destructive. Description adds source table and outlines return fields (probabilities, volume_flow, etc.). Discloses deprecation of include_orderbook_summary. Slight gap: no mention of potential limitations or performance.

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 indicator '[Read]', no extraneous words. 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?

With output schema present, description covers necessary behavior: source, return fields, deprecation, and alternative tool. Complete for this 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%, so baseline is 3. Description mentions 'optional markets[]' aligning with include_markets param, but does not add meaning beyond schema for other params. Acceptable given schema is self-explanatory.

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 specifies verb '[Read]' and resource 'prediction event signal snapshot' with source table and return fields. Differentiates from sibling 'get_market_orderbook' for live book.

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 guidance to use 'get_market_orderbook' when live book is needed, implying this is for historical snapshots. Could also mention when to use over other siblings like search_events, but the key alternative is addressed.

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 and idempotent. The description adds that it uses an OpenSearch index requiring category/status fields, which is useful beyond the annotations. 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?

Single sentence with clear structure, front-loaded [Read] indicator, and efficient use of words. 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?

With output schema present and full parameter descriptions, the description is largely complete. It could clarify the ordering direction, but it's sufficient for an agent to understand the tool's function.

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 parameters are well-documented. The description adds context about the index requirement but does not significantly enhance parameter semantics beyond the schema descriptions.

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 provides a daily ranking by probability rise, with optional filters. The verb 'Get' is implied by the [Read] prefix, and the resource 'ranking' is specific. This distinguishes it from sibling tools like volume delta or 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 does not explicitly specify when to use this tool versus alternatives. It mentions optional filters but lacks guidance on when not to use it or which sibling would be more appropriate for different queries.

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 safe read operations (readOnlyHint, idempotentHint, not destructive). The description adds transparency about the tool rejecting unsupported parameters (history mode, time range, granularity) and explains the internal mechanism for each venue. This complements the annotations well.

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 with two sentences that pack essential information. The 'Read' prefix immediately conveys the action type, and every sentence adds value without redundancy. No unnecessary details.

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 8 parameters (2 required) and an output schema, the description covers the critical constraint (current mode only) and venue details. It does not mention default depth or range, but those are in the schema. The description is adequate for a read tool with good schema coverage.

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%, so the schema already documents all parameters in detail. The description adds a high-level summary of the mode constraint and venue-specific backend logic, but does not clarify parameter semantics beyond what is in the schema. A score of 3 is appropriate as it adds minimal extra 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 'Read current prediction market order book' with a specific verb and resource. It distinguishes itself from sibling tools by explicitly focusing on order book data, and the venue-specific details (CLOB for polymarket, official API for predict_fun) further clarify the purpose.

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 restricts usage to 'mode=current only' and notes that history mode is rejected. It also lists unsupported parameters (start_time, end_time, etc.) that are rejected. However, it does not provide explicit guidance on when to use one venue over the other, though the schema descriptions partially cover that.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that it uses an OpenSearch index requiring category/status fields, which is a prerequisite but not behavioral disclosure. No additional side effects or behaviors are described beyond what annotations provide.

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, well-structured sentence that front-loads the core purpose ('[Read] Daily venue/overall ranking by volume delta (UTC day)') and concisely lists optional filters. 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?

With an output schema present, return values need not be detailed. The description covers purpose, filters, and a technical prerequisite (OpenSearch index exposure). However, it does not explain the ranking logic or what 'volume delta' exactly refers to, leaving some ambiguity.

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% description coverage, with each parameter clearly documented. The description only reiterates that filters are optional and mentions the index requirement, adding minimal 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?

The description clearly states the tool retrieves daily venue/overall ranking by volume delta. The '[Read]' prefix and mention of filters make the purpose unambiguous. It also distinguishes from sibling tools like 'news_prediction_get_fastest_rising_ranking' by focusing on volume delta.

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 lists optional filters but does not explicitly state when to use this tool versus alternatives like 'news_prediction_get_fastest_rising_ranking'. It lacks context on what scenarios favor this ranking metric over others.

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 behavior. The description adds context about the data source (OpenSearch, latest snapshot per pk_id) and optional market summary loading, which goes beyond annotations without contradicting them.

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. The first sentence covers the core function and filters, and the second adds the optional enrichment. 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 the presence of an output schema, the description appropriately focuses on input and behavior. It covers all key aspects: data source, filters, sorting, pagination, and optional fields. 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?

Input schema has 100% coverage with descriptions. The description summarizes the filter categories and highlights the optional 'with_markets' feature, adding value beyond the schema by grouping and contextualizing 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 states it is a read operation to search prediction market events from a specific OpenSearch index, with a clear verb ('search') and resource ('prediction market events'). It distinguishes itself from sibling tools that search other domains (news, social, 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 description specifies the required filters (at least one of query, coin, category) and optional parameters, making it clear when to use this tool. It does not explicitly state when not to use it or provide alternatives, but the context from sibling tools implies appropriate usage.

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