Real-time curated crypto news for AI agents with sentiment, recaps, and search.
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Average 4.4/5 across 7 of 7 tools scored.
Most tools have clearly distinct purposes: listing categories, fetching latest news, searching, getting a single item, recaps, and premium enriched/summary endpoints. Some potential overlap exists between get_enriched_news and get_news_item (both return news item details), but the premium/free distinction helps.
Six of seven tools follow a consistent 'get_' prefix with descriptive nouns (get_categories, get_latest_news, etc.). The exception is search_news, which uses a verb-first pattern but is still readable and predictable.
Seven tools is well-scoped for a crypto news API, covering listing, search, detail retrieval, summaries, and premium tiers without unnecessary bloat. Each tool serves a clear function.
The surface covers core news operations: browse categories, list news, search, fetch by ID, get recaps, and premium enriched/ticker summaries. Minor gaps exist (e.g., no direct source filtering or user-specific endpoints), but the essential workflows are complete.
get_categoriesAInspectList all available news categories with their recap timeframes.
Returns category codes that can be used with get_latest_news, get_news_recap,
and other tools. Each category includes its code, display name, and how
frequently recaps are generated.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It explains that the tool returns code, display name, and recap frequency, and that it lists all categories, which is transparent for a read-only listing operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences long, front-loaded with the primary purpose, and every sentence adds value. It avoids redundancy and is clearly 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?
The tool is simple, has an output schema, and no parameters. The description fully covers the tool's role and relationship to sibling tools, making it complete for an agent to select and use.
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 tool has zero parameters, so the baseline is 4. The description does not need to explain parameters, and the empty schema is consistent. It instead usefully clarifies what the returned data contains, which is more relevant for this tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool's function: listing all news categories and their recap timeframes. It also distinguishes the tool from siblings by explaining that it returns category codes used with other news tools, 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?
The description implies when to use the tool: when you need category codes to use with get_latest_news, get_news_recap, and similar tools. It does not explicitly state exclusions or when not to use it, but for a simple listing tool, this is sufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_enriched_newsAInspectGet enriched news with full AI-generated context and analysis (paid via x402).
This premium endpoint returns the complete news data including:
- long_context: Detailed AI-generated context about the news event
- short_context: Brief contextual summary
- Full entity analysis and token mentions
Payment is handled via the x402 protocol using USDC on Base network.
This tool returns the payment endpoint and instructions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description takes on the full burden. It transparently discloses that the tool is paid via x402 on Base network, returns payment instructions, and details the output structure (long_context, short_context, entity analysis). This is solid coverage, though it does not mention potential failure modes or specifics of the payment flow.
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 well-structured: a leading purpose statement, a bulleted list of returned data, and a clear note on payment. Every sentence contributes value, and it is not overly verbose.
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 it has no parameters and no output schema, the description adequately explains what is returned (long_context, short_context, entity analysis, payment endpoint). It covers the essential return values but does not detail the structure of those fields or any error/edge-case outputs, which is acceptable for a 0-param tool 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 tool has zero parameters, so the baseline is 4. There is nothing for the description to add regarding parameter semantics, and the schema is empty, so no elaboration is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the purpose: 'Get enriched news with full AI-generated context and analysis.' It specifies the resource (news) and the added value (AI context, entity analysis), distinguishing it from siblings like get_latest_news or get_news_item by highlighting the premium, enriched nature.
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 those needing enriched, AI-analyzed news, but does not explicitly state when to use this vs. alternatives, nor does it provide exclusions or alternative suggestions. It mentions 'premium endpoint' as a differentiator but lacks direct guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_latest_newsAInspectGet the latest curated crypto news headlines.
Returns real-time news items with headline, sentiment, categories, and sources.
Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai').
Call get_categories first to see all available category codes.
Args:
category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai').
Omit to get news across all categories.
limit: Number of items to return (1-10, default 5).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| category | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden. It discloses the tool's behavior (returns real-time news with sentiment etc.) and notes that news is 'curated' (not exhaustive). It implies a read-only operation with no side effects, which is appropriate for a 'get' tool.
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, with a clear purpose statement, a return summary, and a concise args section. Every sentence serves a purpose, and the formatting is easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite being a simple read tool, the description covers all essentials: what it returns, how to filter, and a dependency on get_categories. With an output schema present, the description doesn't need to detail return structure, making it sufficient for an agent to select and 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?
The input schema has no descriptions (0% coverage), but the description fully documents both parameters: category with examples and limit with range 1-10 and default 5. This adds significant value beyond the bare 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 opens with a clear verb+resource statement: 'Get the latest curated crypto news headlines.' It also lists return contents (headline, sentiment, categories, sources), which distinguishes it from sibling tools like search_news or get_news_item.
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 usage context: filter by category, omit for all. It explicitly instructs to 'Call get_categories first to see all available category codes,' which is helpful prerequisite info. However, it doesn't specify when to prefer this over siblings like search_news or get_enriched_news, only a brief mention.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_news_itemAInspectGet a specific news item by its ID.
Returns the full free-tier details for a single news item including
headline, sentiment, categories, sources, and tweet URL.
Args:
id: The news item ID (returned in results from get_latest_news or search_news).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the return payload (headline, sentiment, categories, sources, tweet URL) but does not mention error handling, auth requirements, or read-only nature. It adds some behavioral context but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the primary purpose, followed by return details and a parameter explanation. Every sentence serves a purpose with no redundancy or 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?
For a single-parameter retrieval tool with no output schema, the description covers the essential aspects: what it does, what it returns, and where the parameter comes from. It lacks information on edge cases (e.g., invalid ID), but given the tool's simplicity, it 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?
The schema only defines 'id' as a required string with no description. The description compensates by explaining the id's origin ('returned in results from get_latest_news or search_news'), which adds meaningful context beyond the schema. It does not specify an exact format, but for a simple ID this is adequate.
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: 'Get a specific news item by its ID.' It distinguishes itself from sibling tools that list or search news by focusing on single-item retrieval by ID.
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 when to use it (when you already have an ID from get_latest_news or search_news) and provides clear context via the Args section. However, it lacks explicit commentary on when not to use it or alternatives, falling 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.
get_news_recapAInspectGet an AI-generated news recap/summary for a specific category.
Returns a concise narrative summarizing the most important recent news
for the given category. Great for getting up to speed quickly.
Args:
category: Category code (required). Use get_categories to see options.
Popular choices: 'crypto', 'bitcoin', 'ethereum', 'defi', 'ai', 'macro'.
timeframe: Time window for the recap. Use '1h' for crypto/macro (updated hourly),
'8h' or '24h' for other categories. Default '12h'.
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | ||
| timeframe | No | 12h |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the output is AI-generated, returns a narrative summary, and that some categories update hourly (timeframe guidance). This gives useful behavioral context, though it does not mention error handling, authentication, or potential variability of AI-generated content.
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, with a clear opening sentence, a return description, and a well-formatted Args section. Every sentence provides value with no redundant content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 2-parameter tool with no output schema and no annotations, the description covers purpose, return type, parameter usage, and practical tips. It is complete enough for an agent to select and invoke the tool correctly without further 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?
Schema coverage is 0%, so the description fully compensates by explaining the category parameter (required, with popular choices and a pointer to get_categories) and timeframe (with recommended values per category and default). Both parameters are meaningfully described 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 'Get an AI-generated news recap/summary for a specific category' with a specific verb and resource. It distinguishes from sibling tools by emphasizing a 'concise narrative summarizing the most important recent news', making its summary purpose distinct from news listing or item retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear usage context for both parameters ('Use get_categories to see options', timeframe recommendations) and an overall use case ('Great for getting up to speed quickly'). However, it does not explicitly state when not to use this tool versus alternatives like get_latest_news or get_enriched_news.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ticker_summaryAInspectGet a 24-hour AI-generated summary for any crypto ticker or topic (paid via x402).
Returns decision-grade bullet points combining Gloria's curated news with real-time web search. Designed for fund managers and trading agents.
Payment is handled via the x402 protocol using USDC on Base network. This tool returns the payment endpoint and instructions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the payment protocol (x402, USDC on Base), the return format (decision-grade bullet points, payment endpoint, instructions), and the data sources (Gloria's curated news, real-time web search). It stops short of detailing potential side effects, rate limits, or requirements, but for a read-only summary tool this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the core purpose. Each sentence adds useful information: purpose, content, target audience, and payment process. It is efficient with no fluff or repetition.
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 is moderately complex with payment integration and no output schema. The description explains what it returns (bullet points, payment endpoint/instructions), who it serves, and the payment mechanics. However, it fails to clarify how the user specifies the ticker/topic given the empty parameter schema, and the '24-hour' scope is slightly ambiguous (last 24 hours vs a rolling window). These gaps prevent a perfect score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero properties, but the description adds semantic meaning by stating 'any crypto ticker or topic' as the subject. This clarifies what the tool operates on, though it leaves unclear how the ticker/topic is actually passed given the empty parameter list. Baseline for zero parameters is 4, and the description adds value without fully resolving invocation ambiguity.
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 'Get a 24-hour AI-generated summary for any crypto ticker or topic', specifying the verb (get), resource (summary), and scope (ticker/topic). It also differentiates from sibling news tools by emphasizing AI-generated decision-grade bullet points combining curated news and web search, rather than raw news retrieval.
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 clear context: 'Designed for fund managers and trading agents' and explains the paid x402 mechanism. However, it does not explicitly mention when not to use this tool or name alternative sibling tools, leaving exclusion criteria implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_newsAInspectSearch curated crypto news by keyword.
Searches across all news items for matching content. Returns headlines,
sentiment, categories, and sources.
Args:
query: Search keyword or phrase (e.g. 'ETF', 'SEC', 'Uniswap').
limit: Number of results to return (1-5, default 5).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It states the search scope ('across all news items') and the return data ('headlines, sentiment, categories, and sources'), which adds useful context. However, it does not explicitly confirm that the operation is read-only, nor does it mention any sorting, pagination, or error behavior. For a search tool, the description is adequate but not rich.
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 well-structured. It opens with a one-sentence summary, then a brief scope statement, a return field list, and an Args section. Every sentence contributes meaningful information, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool with an output schema, the description covers the core behavior and parameters adequately. It mentions return fields and parameter constraints. However, it lacks explicit guidance on when to use this tool over sibling tools like get_latest_news or get_enriched_news, and does not mention potential edge cases such as no results or response pagination. The description 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 provides explicit explanations for both parameters, including examples for query ('ETF', 'SEC', 'Uniswap') and range/default for limit ('1-5, default 5'). This fully compensates for the input schema, which has 0% description coverage. The added detail goes beyond the schema's type and default, making the parameters 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?
The description clearly states the tool's function: 'Search curated crypto news by keyword.' It specifies the resource (crypto news), the action (search), and the method (by keyword). It also lists the returned fields (headlines, sentiment, categories, sources), which helps differentiate it from siblings like get_latest_news or get_news_item.
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 when to use the tool: 'Search curated crypto news by keyword' and 'Searches across all news items for matching content.' It provides example queries, but it does not explicitly compare with sibling tools or state when not to use it. Context signals show siblings, but the description does not name alternatives, so it is clear but lacks exclusions.
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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