Gate News MCP

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds valuable behavioral context: it states it uses real-time Tavily search, aggregates data, returns completeness status, and explicitly declares 'Read-only public research data. No account access, no order placement or fund transfers.' This goes beyond the basic annotation and gives the agent a clear understanding of side effects and limitations.

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

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

The description is concise and front-loaded: it opens with '[Read]' and the main purpose, then lists return components, clarifies the tool's role, and ends with safety disclaimers. Every sentence adds value without redundancy or fluff, making it well-structured and easy to parse.

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 (5 parameters, output schema, data aggregation role), the description is complete. It explains what the tool does, what it returns, how it should be used (with downstream reasoning), and its safety profile. Since an output schema exists, the description does not need to detail return value formats, and it sufficiently covers the essential 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 schema covers 100% of parameters with descriptions for each, so the baseline is 3. The description does not add significant per-parameter semantics beyond what the schema provides; it only mentions time window and coin at a high level. Since the schema fully documents parameters, the description does not need to compensate.

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 ('Explain') and resource ('what drove a crypto asset's price move in a given time window'), which distinguishes it from siblings. It also specifies the output components (summary, high-priority events, internal pool items, completeness status), making the purpose unambiguous and well-scoped.

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 gives clear context on when to use the tool: for explaining market moves, and clarifies its role as a data aggregator where the downstream agent performs final reasoning. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to choose this tool 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?

Adds significant behavioral context beyond annotations: 'Unknown id returns not found,' 'persisted impact_direction,' 'No account access, no order placement or fund transfers,' and 'Not investment advice.' This enriches the readOnlyHint with specific safety and error semantics, with no contradiction.

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

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

The description is compact and front-loaded with '[Read]'. Each sentence adds distinct value: scope, alternative tool, error behavior, and safety disclaimers. No redundant wording.

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

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

For a simple one-parameter read tool, this covers purpose, usage, error handling, and safety. Output schema exists, so omitting return details is acceptable. The description is fully self-sufficient.

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 already fully describes event_id as an opaque digest id from get_latest_events, so the description adds little beyond restating 'one event_id only.' The description does not clarify the parameter beyond what the schema provides, so baseline 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?

Clearly states it retrieves full detail for a single event_id, using a specific verb and resource. Distinguishes itself from get_latest_events by noting that filtered lists/timelines should use the sibling tool.

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

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

Explicitly directs users to get_latest_events for filtered event lists or timelines, implying this tool is for retrieving complete details of a specific known event. Also mentions unknown id behavior, providing clear usage context.

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

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

Annotations already declare readOnlyHint/idempotentHint, but the description adds valuable context beyond that: it notes the data is 'persisted', states 'No account access, no order placement or fund transfers', and 'Not investment advice'. These are behavioral/contextual traits not in the annotations, though it doesn't cover pagination behavior 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 front-loaded with the core purpose, then delivers usage guidance and safety context in short, purposeful sentences. No wasted words; each clause adds distinct 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?

The description is complete for this tool given the rich schema and output schema. It covers purpose, row structure, filtering options, alternatives, and operational context. Pagination and parameter details are already in the schema, so the description doesn't need to repeat 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% with richly descriptive parameter definitions, so the baseline is 3. The description adds marginal value by summarizing 'Optional direction filter' and mentioning row contents, but it does not meaningfully deepen parameter understanding beyond what the schema already provides.

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 'Filtered event list or timeline' with specific row contents, and explicitly distinguishes from siblings by pointing to get_event_detail for single-event details and search_news for headlines. The verb+resource 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?

The description provides explicit alternatives: 'One event_id detail -> get_event_detail' and 'Headline/news feed -> search_news', giving clear when-to-use vs alternatives. It also sets context with 'Read-only public research data' and disclaimers, which helps the agent decide appropriately.

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 destructiveHint, but the description adds valuable behavioral details: UTC0 timestamp semantics, lookup priority, no report regeneration, no account access, no order placement, and 'not investment advice.' These go well beyond the structured annotations.

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

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

The description is dense but every sentence adds value: operation, lookup priority, timezone handling, lack of regeneration, safety profile, and disclaimer. It is front-loaded with the core action and has no filler or redundant restatements.

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 read-only lookup tool with a rich output schema and comprehensive annotations, the description is complete. It covers timezone behavior, lookup semantics, safety boundaries, and explicitly states what the tool does not do, leaving no significant gaps for an agent to misinterpret.

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 baseline is 3. The description reiterates lookup priority already present in the schema (report_id > event_id > latest) but adds no new parameter-level semantics beyond what the schema documents.

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 starts with a specific verb and resource: 'Query a stored market-move attribution report by symbol...' It clearly identifies the exact operation and distinguishes it from sibling tools like news_events_list_market_move_reports by focusing on report lookup vs. listing.

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

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

The description explains when to use the tool (querying stored reports by symbol, report_id, or event_id) and provides an explicit exclusion: 'never requests report regeneration and does not expose is_make_new.' However, it does not name specific alternative tools for regeneration or listing, so it stops short of full alternative 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 goes well beyond the readOnlyHint annotation by explaining that updated_at can be later than event_time, that the query is read-only, timezone conversion behavior, and that it involves no account access or order placement. This gives substantial 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 front-loaded with the core purpose and each subsequent sentence adds meaningful context about timezone handling, update behavior, and safety. Minor redundancy in the list of disclaimers but overall compact 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?

With an output schema present and annotations marking read-only/idempotent, the description fully covers necessary operational details: filtering, sorting, timezone handling, update asymmetry, and safety. No major gaps remain for selecting and invoking 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 coverage is 100%, so the schema already documents each parameter. The description adds value by explaining the inclusive UTC0 updated_at range semantics and the event_time descending sort, which are not in the schema, plus the possibility that updated_at exceeds event_time.

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 specifies a clear action (List) and resource (stored market-move attribution reports) with a defined scope (one symbol) plus filtering and sorting behavior. It is easily distinguishable from sibling tools like the singular get_market_move_report.

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 conveys when to use this tool: to list multiple reports for one symbol within an updated_at range. It does not explicitly name alternatives or exclusions, but the context is sufficient to imply selection over singular get/report tools.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable non-obvious behavioral details: 'No account access, no order placement or fund transfers' and 'Not investment advice,' which go beyond annotation hints and clarify safety and scope.

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

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

The description is concise, front-loaded with '[Read]', and every sentence adds distinct value: data scope, alternatives, and behavioral caveats. 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?

With 8 parameters fully documented in the schema, a rich output schema present, and comprehensive annotations, the description covers the remaining contextual gaps: data source, exclusions, and non-financial advice. It is complete and self-sufficient.

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 clearly. The tool description adds no parameter-specific semantics beyond what the schema provides, matching the baseline of 3 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?

Description clearly states the tool retrieves venue-published exchange notices (listings, delistings, maintenance) and explicitly distinguishes from search_news for media rumors or general headlines. It uses a specific verb and resource, 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 explicitly directs users to search_news for media rumors or general crypto headlines, providing a clear alternative. It also reiterates in the query parameter description that this tool is not a substitute for venue-published listing APIs, giving precise usage boundaries.

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, and destructiveHint=false. The description adds valuable context beyond these: it confirms 'Read-only public research data', states 'No account access, no order placement or fund transfers', and includes a 'Not investment advice' disclaimer. It also previews output contents. However, it does not describe edge-case behaviors like empty results for unknown tickers (which is left to the schema). Overall, it adequately supplements annotations.

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

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

The description is three sentences, each earning its place: first states what the tool returns, second gives alternative tool guidance, third covers safety/disclaimer. It is front-loaded with the core purpose and avoids fluff. Perfectly concise for the information it conveys.

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 moderate complexity, rich annotations, and full schema coverage, the description is complete. It covers purpose, output contents, alternative use cases, and safety boundaries. The output schema handles return value details, so the description does not need to explain them. No gaps are apparent.

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 baseline is 3. The description itself adds minimal parameter-specific detail beyond mentioning 'one coin' and 'latest 4h' in the purpose statement, which aligns with 'coin' and 'window'. It does not explain limit, platforms, or their allowed values; however, the schema descriptions are thorough. Thus the description does not need to compensate but also does not add extra semantic 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 uses a specific verb ('Get') and clearly identifies the resource: top 2-4 social discussion themes for one coin over the latest 4h, including direction, influence, sentiment, platforms, and evidence posts. It explicitly differentiates from sibling tools by naming search_ugc and search_x for other use cases, making the tool's scope unmistakable.

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: 'For arbitrary social search use search_ugc; for X/Twitter-only narrative research use search_x.' It also clarifies read-only and non-transactional nature, which helps an agent choose this tool over trading or account-related tools. This is exemplary usage differentiation.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context beyond that: it mentions 'display eligibility' as an output aspect, and explicitly states 'No account access, no order placement or fund transfers, not investment advice.' This enriches the agent's understanding of what the tool does and does not do.

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-loaded with the main purpose and outputs. Each sentence serves a distinct role: purpose, alternatives, and safety/context. 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?

Given the presence of a rich output schema, comprehensive annotations, and full schema parameter coverage, the description provides the necessary context for tool selection and invocation. It disambiguates from many sibling tools, covers usage boundaries, and includes safety caveats, making it complete for an agent.

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 baseline is 3. The description does not individually detail parameters (coin, window, platforms) but schema already fully documents them with examples and constraints. The description's mention of '24h' and 'platform breakdown' aligns with schema fields without adding new semantics beyond it.

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

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

The description opens with '[Read]' and a specific verb+resource: 'Get a coin's 24h multi-platform social mention burst signal, growth, sentiment direction, platform breakdown, and display eligibility.' This clearly states what the tool does and distinguishes it from siblings by naming alternative tools for different use cases (get_social_sentiment, 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 explicitly provides when-to-use and when-not-to-use guidance: 'For general sentiment ratios and sample tweets use get_social_sentiment; for individual social discussions use search_ugc.' It also adds context that this is read-only public research data with no order placement or fund transfers, clarifying its safe usage scope.

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

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

Annotations already declare readOnlyHint true and destructiveHint false, but the description adds meaningful context beyond that: 'No account access, no order placement or fund transfers. Not investment advice.' This clarifies the tool's non-financial, non-transactional nature, which is useful for an agent deciding whether to invoke it. It does not contradict annotations.

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

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

The description is three concise sentences, front-loaded with a clear '[Read]' tag and a direct summary. Every sentence adds value—purpose, alternatives, and safety disclaimers—with no wasted words. It is well-structured for quick scanning by an AI agent.

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 (2 optional parameters, with an output schema), the description fully covers purpose, usage alternatives, safety, and scope. It mentions the key output aspects (sentiment, split, mentions, tweets) and clearly differentiates from sibling tools. No critical behavioral details are missing.

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 detailed descriptions for both `coin` and `time_range`. The overall description repeats these details (e.g., 'overall sentiment, positive/negative split, mention count') but does not add new parameter-specific information beyond what is already in the schema. Thus, it meets the baseline for high schema coverage without adding significant 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 the exact action: 'Aggregate per-coin social sentiment for a time range' and enumerates specific outputs (sentiment, positive/negative split, mention count, sample tweets). It also explicitly distinguishes from sibling tools by directing search-oriented tasks to search_x and search_ugc, 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 gives explicit alternatives: 'X/Twitter post search or tweet-level evidence -> search_x' and 'Multi-platform social thread search -> search_ugc', which tells the agent when to use other tools instead. It also clarifies the tool's scope with 'Read-only public research data' and 'No account access, no order placement or fund transfers', providing clear when-to-use context.

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

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

The description adds behavioral context beyond annotations: 'No account access, no order placement or fund transfers. Not investment advice.' This complements the readOnlyHint and openWorldHint annotations without contradicting them, offering useful operational caveats.

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 four sentences, front-loaded with the primary action and purpose, followed by disambiguation and caveats. Every sentence contributes value: purpose, alternatives, and behavioral boundaries. No fluff 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?

Given the tool's complexity (13 params, 0 required) and rich schema plus output schema, the description provides sufficient orientation. It clarifies scope, distinguishes siblings, and states constraints. No critical information is missing for an agent to use it effectively.

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 baseline is 3. The description does not elaborate on parameters; it relies entirely on the schema's detailed per-parameter descriptions. No added semantic value beyond what the schema already provides.

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

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

The description states a specific verb and resource: 'Search the platform news index for headlines, news items, and briefing-style result lists.' It clearly distinguishes the tool from siblings by referencing web_search and get_latest_events as alternatives, 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?

Explicit when-not-to-use guidance is given: 'Open-web research with synthesized answers and cited external pages -> web_search. Event catalog with event_id -> get_latest_events.' It also sets context as read-only public data with no account access or transactions, helping an agent choose correctly.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false; the description reinforces 'Read-only public research data' and adds 'No account access, no order placement or fund transfers.' It also discloses the dual vector/OpenSearch behavior based on query/coin presence, going beyond annotation coverage.

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 dense yet compact, front-loaded with '[Read]' and platform list, and every clause conveys a necessary usage, exclusion, or safety point. No filler words or redundant references to schema/sibling tools.

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 9-parameter schema with 100% coverage, output schema, and comprehensive annotations, the description adds sufficient context for selection and invocation, including behavioral modes and exclusions. It is complete for an agent to decide 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 covers 100% of parameters, including query/coin interplay and domain clarifications. The description mostly repeats this information, adding no new parameter-level meaning, 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 it handles Reddit/Discord/Telegram/YouTube-style UGC and explicitly differentiates from search_x, search_news, and get_latest_events. The '[Read]' prefix and platform enumeration make the scope 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?

Provides explicit alternatives: X/Twitter narrative -> search_x, headlines -> search_news, structured events -> get_latest_events. It also specifies the query/coin mode requirements and what is invalid (both empty), giving clear when-to-use/when-not-to-use 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, openWorldHint, idempotentHint, and destructiveHint: false. The description adds context by stating 'Read-only public research data. No account access, no order placement or fund transfers. Not investment advice.' This clarifies the safety profile beyond the annotations, though it does not detail error behavior or output structure (covered by output schema).

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 dense but front-loaded with the core purpose, then offers routing guidance and safety disclosures. Every sentence adds value with no fluff. It is slightly long given the complexity, but appropriately sized for a tool with dual xAI/platform fallback modes.

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 19-parameter tool with output schema and rich annotations, the description is comprehensive. It explains the primary goal (tweet-level evidence), fallback behavior, alternative tools, read-only nature, and non-investment-advice status. Combined with schema and output schema, an agent has sufficient context 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% (19/19 parameters documented), so the baseline is 3. The tool description itself does not add parameter-level meaning, but the schema descriptions already explain xAI vs platform fallback semantics (e.g., time_range takes precedence, handle filters mutually exclusive). The description complements without redundancy.

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 a specific action (search and analyze X/Twitter discussions), identifies the resource (tweet-level evidence and cited posts), and differentiates from siblings by explicitly directing users to get_social_sentiment, web_search, and search_ugc for other use cases. This provides unambiguous 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 provides explicit routing instructions: aggregate mood/sentiment to get_social_sentiment, open-web pages to web_search, multi-platform social search to search_ugc. It also clarifies 'Read-only public research data' and 'No account access' which sets expectations for when to choose 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?

Annotations already declare readOnlyHint=true and openWorldHint=true, but the description adds crucial context: 'Read-only public research data,' 'No account access, no order placement or fund transfers,' and 'Not investment advice.' It also discloses the synthesized-answer-with-citations output style, going well beyond the structured annotations.

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

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

The description is three sentences with a clear structure: the main function, targeted alternatives, and hard limitations. Every sentence carries essential information with zero 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?

Given the tool's open-web nature, the description fully covers what it does, when to use alternatives, and important constraints. An output schema exists, so return values are already specified. The description is complete for an agent to select and 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 description coverage is 100%, and the schema already provides rich per-parameter semantics, including cross-references to search_news and search_x. The tool description itself does not add further parameter-level detail, 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 states a specific verb+resource: 'Search the open web and return a synthesized answer with cited external pages.' It clearly distinguishes itself from siblings by explicitly pointing to search_news and search_x for alternative use cases, making the purpose unmistakable.

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: 'Built-in headline lookup, news-item search, or briefing-style news list -> search_news. X/Twitter-only discussion or tweet evidence -> search_x.' It also lists exclusions (no account access, no order placement, not investment advice), fully clarifying when not to use the 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?

Annotations already indicate read-only, idempotent, non-destructive. The description adds valuable behavioral disclosures: depth_summary always null, include_orderbook_summary ignored, daily_ranking conditionally configured, and no trading capabilities. This goes beyond the annotations.

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

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

The description is dense but well-structured, front-loading the read intent and core lookup key. Each sentence earns its place, covering key behavioral notes concisely. Slightly long but not bloated.

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 5 params, one required, the description covers return fields, defaults, null behaviors, related tools, and usage boundaries. With an output schema present and annotations, this is fully complete for an agent to invoke 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%, so baseline is 3. The description adds semantic nuance: window defaults and allowed values, event_ref format with colon splitting, include_markets default behavior, and deprecation of include_orderbook_summary. This enriches understanding 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 fetches a single event signal by event_ref, with specific resources (dws_external_event_signal_hf) and output fields. It distinguishes itself from siblings by explicitly directing to get_market_orderbook for depth and search_events for discovering event_refs.

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?

Clear context is given: read-only research data, no account access, and when to use alternatives (get_market_orderbook for live depth, search_events to find event_ref). It does not explicitly list 'when not to use' but the pointers are concrete.

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 annotations, the description discloses significant behavioral details: it drops rows missing open_mid_probability_utc or probability_delta_today, requires predictionRankIndex, and states it is read-only public research with no account access or order placement. These add real value beyond the structured annotations.

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

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

The description is front-loaded with the main purpose and is information-dense. There is slight redundancy with 'Read-only' given the annotation readOnlyHint=true, but the additional safety disclaimers and row-drop note justify their inclusion.

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 full output schema, rich annotations, and a description covering the ranking behavior, filters, row-drop edge cases, and prerequiusite, the tool is sufficiently specified for an agent to invoke correctly. Nothing critical is omitted.

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 baseline is 3. The description adds a cross-reference ('same as volume_delta ranking') and row-drop context, but it does not add per-parameter semantics beyond what the schema already provides.

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 'Daily venue/overall ranking by probability rise' with optional filters. This distinguishes it from the sibling volume-delta ranking tool by the sorting metric (probability rise vs volume delta), making its purpose specific and unique.

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 usage context: 'Optional venue[], category, status (same as volume_delta ranking)' tells users the filters are shared with a sibling tool, and 'Requires predictionRankIndex' gives a prerequisite. However, it does not explicitly say when to prefer this tool over the volume-delta ranking tool, only implies it.

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 annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint: false), the description discloses partial failure behavior, config-missing handling (partial_not_configured), rejection of unsupported fields, and nullability of outputs. It also emphasizes no account access or order placement, aligning with the read-only annotation without contradicting 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 front-loaded with the core purpose, then methodically details venue-specific logic, error modes, and limitations. Every sentence adds necessary information, and the use of semicolons and line breaks keeps it scannable despite the breadth of detail.

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, two venues, partial failure modes, config dependencies), the description leaves no significant gap. It covers error handling, config requirements, unsupported inputs, and even points to sibling tools for alternative use cases. The output schema exists, so return-value details are not 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?

Although schema coverage is 100%, the description adds critical meaning: polymarket market_id must be venue_market_id; predict_fun requires official numeric id; mode is limited to 'current'; depth range 1-20. It also clarifies that unsupported params are rejected, which is not fully captured in the schema alone.

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 live current order book, explicitly scoped to mode=current. It differentiates from sibling tools with concrete alternatives like 'Event list/signal -> search_events / get_event_signal,' making its 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?

It provides explicit when-to-use guidance, including venue-specific prerequisites (opensearch.predictionMarketIndex for polymarket, predictFunAPIKey for predict_fun) and alternatives for other data needs. The description also warns about unsupported parameters (history, granularity, time, page_token), preventing misuse.

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 meaningful behavior beyond the annotations: it reveals the requirement for opensearch.predictionRankIndex, states this is public research data, and explicitly confirms no account access or order placement. This goes beyond the readOnlyHint and destructiveHint annotations, though it does not mention rate limits or error behavior. There is no contradiction with annotations.

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

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

The description is composed of several short sentences that are all relevant, front-loading the core purpose ('Daily venue/overall ranking by volume delta'). It includes necessary context such as the index requirement and disclaimers. While it is a bit longer than minimal, every sentence adds value, earning a 4 rather than a 5 for extreme brevity.

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 output schema exists and annotations provide safety traits, the description is largely complete. It covers the purpose, optional filters, a backend requirement, and restrictions. It does not explain pagination or the exact return structure, but the output schema handles that. The description is sufficient for an agent to 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?

The schema covers 100% of the 5 parameters, so the baseline is 3. The description repeats parameter information (venue, category, status) without adding new semantics beyond what the schema already states. It adds only marginal clarity, like 'exact term' for category, but this is already in 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 that this tool provides a daily ranking by volume delta, with optional venue/overall scope. It specifies the resource (ranking data) and the verb (get), and the [Read] prefix aligns with the read-only nature. This distinguishes it from siblings like news_prediction_get_fastest_rising_ranking, which focuses on a different metric.

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 gives clear context on when to use the tool (for volume delta rankings) and explicitly lists optional filters, but it does not mention alternative tools or when not to use it. While the sibling tool names are available in context, the description itself lacks explicit differentiation, 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?

Beyond the readOnlyHint annotation, the description discloses critical behaviors: the unique key composition, sort semantics (recently_listed uses create_time, volume_delta_today maps to total_volume_usd_24h), category matching against two fields, collapse behavior, and the source for with_markets. It also explicitly states 'Read-only public research data. No account access, no order placement or fund transfers.' This provides substantial context beyond annotations.

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

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

The description is dense with high-value information, front-loaded with the core purpose and then systematically covering filters, sorting, and constraints. Every sentence adds operational detail, and there is no filler or repetition of schema fields. It is long but appropriately so for the complexity.

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 9 optional parameters, an output schema, and read-only intent, the description covers all critical aspects: data source, filtering options, sorting behavior, edge cases (unsupported_filter), related external tools, and safety declarations. The presence of an output schema means return values need no elaboration, and the description fills all other 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?

Even though schema coverage is 100%, the description adds crucial parameter semantics not fully captured in the schema: the interaction between coin and query/category (coin-only unsupported_filter), the meaning of status_tags from status_json_array, the sort mapping for volume_delta_today, and the dual-field category match. This significantly enhances the agent's ability to use parameters correctly.

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 'Search prediction events' with a specific index and alias, and distinguishes itself from sibling tools by explicitly referencing get_event_signal for detail and get_market_orderbook for orderbook. The verb 'Search' plus the resource and scope make 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 provides explicit guidance on when to use this tool versus alternatives: 'Detail -> get_event_signal (external); orderbook -> get_market_orderbook.' It also notes the unsupported_filter for coin-only queries, which helps the agent avoid invalid usage. This exceeds the 'explicit when/when-not/alternatives' criterion.

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