Real-time corroborated news events + 5-year archive, for agents. Free tier, no key.
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Average 4.4/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: stats, single event fetch, event querying, source listing, and raw article search. The primary tool (get_events) is explicitly differentiated from search_articles, and get_event is for drilling into a specific event. No overlap or ambiguity.
The naming mostly follows a verb_noun pattern (get_event, get_events, list_sources, search_articles), but 'corpus_stats' deviates slightly (noun_noun). Still predictable and readable, so only a minor inconsistency.
Five tools is a well-scoped set for a news aggregation service. Each tool covers a distinct need (overview, query, detail, sources, raw search) without redundancy or bloat.
For a read-only news API, the surface is comprehensive: users can generate stats, explore deduped events, fetch individual event details, list sources, and search raw articles. No obvious missing operations for the domain.
corpus_statsACorpus size and freshness: source / article / event counts and the latest article timestamp.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds that the tool returns counts and a timestamp, which aligns with annotations. No additional behavioral details (e.g., caching, empty corpus handling) are provided, but annotations cover the safety profile adequately.
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 concise sentence that front-loads the purpose and key outputs. Every word is meaningful with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description lists the main output elements (counts and latest timestamp). Minor details like timestamp format or behavior for empty corpus are omitted, but for a simple stats tool, this is reasonably complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters (schema coverage 100% with empty properties). Baseline for 0 parameters is 4. The description does not need to explain parameters, and is clear about what the tool returns.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns corpus size and freshness metrics (source/article/event counts and latest article timestamp). This distinguishes it from sibling tools like get_event, get_events, list_sources, and search_articles, which operate on specific items rather than aggregate stats.
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 implies usage for obtaining overall corpus statistics, but does not explicitly state when to use this tool versus alternatives (e.g., for detailed event queries, use get_events). No guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eventAFetch a single event by id, including every corroborating article (source, url, timestamp).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Event id from get_events |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds behavioral context about the return structure (includes corroborating articles with source, url, timestamp), which goes 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 a single, efficient sentence with no wasted words. Every part 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?
For a single-parameter retrieval tool, the description is complete: it specifies the input (id), output content (corroborating articles details), and safety (annotations). No output schema, but the description covers the return structure adequately.
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 the parameter 'id' is described as 'Event id from get_events'. The description does not add any additional meaning to the parameter beyond what the schema provides; it only describes the return format.
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 verb 'Fetch' and the resource 'a single event', specifying that it includes every corroborating article with source, url, and timestamp. This distinctly distinguishes it from sibling tools like get_events (list) or search_articles.
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 implies usage when you need a specific event by id, but does not explicitly state when to use alternatives like get_events for multiple events or search_articles. No exclusions or prerequisites are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eventsAQuery the deduped news event graph by theme, source, category and time window. Returns events (many articles about one happening collapsed into one), each with a corroboration count and confidence score. This is the primary tool — prefer it over raw articles. For headlines, briefings, or 'what's happening' asks, pass min_sources: 2 (or 3 for high signal) — the raw recency feed is dominated by single-outlet local stories without it. ONE call is normally enough: trust each event's corroboration (distinct outlets), confidence (0-1; 1.0 = fully corroborated), and sources list as-is, and present the top events directly, including each event's url as its link — do NOT call get_event per item or re-verify counts (only drill in when the user wants EVERY outlet's link for one story). The corpus spans 260+ global outlets and a five-year archive (history visible depends on tier).
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text theme/keyword to match on headline + summary | |
| to | No | Only events active on/before this date. ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| from | No | Only events active on/after this date. ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| limit | No | Max events (default 15) | |
| source | No | Source slug, e.g. 'coindesk' (see list_sources) | |
| category | No | Source category | |
| semantic | No | Rank by meaning (embedding similarity) instead of keyword match — requires q; better for concepts ('monetary easing') than exact strings | |
| min_sources | No | Only events corroborated by at least this many distinct outlets — use 2-3 for briefings/headlines to skip single-source noise |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnlyHint, idempotentHint, etc.), the description discloses the deduplication behavior (many articles collapsed into one event), return fields (corroboration count, confidence score, sources list, url), the nature of the confidence score (0-1, 1.0 = fully corroborated), and the corpus scope (260+ global outlets, five-year archive, tier-dependent history). It also flags the raw recency feed's noise issue and advises on how to filter it. These behavioral details far exceed what annotations provide and are directly actionable.
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 earns its place: the first sentence states the action and resource, the second defines return values, then usage guidance, anti-pattern warning, and scope. It front-loads the core purpose and then layers practical advice. There is no filler or redundancy; the length is justified by the tool's complexity and the need to prevent common misuses.
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, deduplication, corroboration scoring, multiple use cases), the description covers all critical aspects: what it returns, how to interpret confidence/corroboration, when to use or avoid alternatives, how to filter for high signal, and the corpus scope. With no output schema, it still describes the key return fields. An agent could call this correctly without additional information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all 8 parameters with good descriptions, so baseline is 3. The description adds valuable usage context beyond the schema: it explains that min_sources should be 2-3 for high-signal asks, that semantic requires q and is better for concepts, and interprets what the returned fields mean. This enriches parameter understanding, especially for min_sources and semantic, though it doesn't add syntax-level detail for every param. That raises it to a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a precise action: 'Query the deduped news event graph by theme, source, category and time window.' It clearly explains the core resource (deduped event graph vs. raw articles) and differentiates from siblings by calling itself 'the primary tool' and explicitly warning against calling get_event per item. This gives an agent unambiguous understanding of what the tool does and how it differs from search_articles and get_event.
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 when-to-use guidance: 'prefer it over raw articles' for event-level questions, and instructs to pass min_sources: 2 or 3 for 'headlines, briefings, or what's happening asks.' It also states when NOT to use it: 'do NOT call get_event per item or re-verify counts (only drill in when the user wants EVERY outlet's link for one story).' These usage directives are concrete and unambiguous, leaving no doubt about selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_sourcesAList all news sources Newsflash tracks, with categories and live article counts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds that it returns categories and article counts, which is useful. It does not mention pagination or size limits, but with no parameters, this is minor.
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, no fluff, front-loaded with key information. Every word is meaningful.
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?
Description explains what is returned (categories, article counts) but could mention that the list is complete and unfiltered. For a simple list tool without output schema, it is largely 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?
No parameters exist, and schema coverage is 100%. The description correctly adds no parameter-level details. Baseline 4 is appropriate for zero-parameter tools.
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 lists all news sources with categories and live article counts. It is distinct from sibling tools like search_articles (search) and get_events (events).
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 implies this is for retrieving a full list of sources, but does not explicitly specify when not to use it or compare to alternatives. Given the simplicity, the purpose is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_articlesASearch raw articles (not collapsed into events). Use when you want the underlying items or need per-outlet coverage rather than deduped happenings.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Free-text keyword on headline + summary | |
| to | No | ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| from | No | ISO 8601 date/time, e.g. 2026-07-01 or 2026-07-01T00:00:00Z | |
| limit | No | Max articles (default 25) | |
| source | No | Source slug | |
| category | No | Source category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context about returning raw, per-outlet articles, which aligns with annotations and provides additional behavioral insight. 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?
The description is two sentences, front-loaded with the purpose, and contains no unnecessary words. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 6 parameters with full schema descriptions, but there is no output schema or return value description. Given that it's a search tool with read-only intent, the description is nearly complete, but a brief note on response structure or examples would improve completeness.
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?
All six parameters have descriptions in the schema (100% coverage). The tool description does not add any additional parameter information beyond what the schema already provides, so it adds no extra value for parameter semantics.
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 raw articles' and distinguishes from events by explicitly saying 'not collapsed into events' and contrasting with 'deduped happenings', which differentiates it from sibling tools like get_events.
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: 'Use when you want the underlying items or need per-outlet coverage rather than deduped happenings.' This clearly indicates the context and alternatives (sibling tools that return collapsed events).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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