Real-time curated crypto news for AI agents with sentiment, recaps, and search.
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Average 4.1/5 across 7 of 7 tools scored.
Most tools have distinct purposes, but get_enriched_news lacks clear parameters, making it ambiguous whether it returns a specific item, the latest, or category-based news. This could cause confusion with get_latest_news or get_news_item.
All tool names follow a consistent 'get_<noun>' pattern, making the naming predictable and easy to interpret.
Seven tools is well-scoped for a crypto news API, covering essential operations without being too few or too many.
The tool set covers the core workflow: listing categories, fetching latest news, searching, getting details, recaps, and premium enrichments. No obvious gaps for a read-only news service.
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?
Since no annotations are provided, the description carries the full burden. It discloses that the tool lists categories and their recap frequencies, and that output is used by other tools. It could mention return structure in more detail, but with an output schema available, the description adds meaningful context beyond 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 two sentences with no wasted words. It front-loads the core purpose (list all available news categories) and immediately provides actionable context (returns codes for other tools, includes code, name, recap frequency).
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 zero parameters, 100% schema coverage, and an existing output schema, the description is complete. It tells the agent what information is returned and how it integrates with sibling tools, which is sufficient for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with 0 parameters, so the schema already fully covers all input. The description adds value by explaining what the tool returns and how it relates to other tools, going beyond the empty 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 lists all available news categories with their recap timeframes, which is a specific verb+resource combination. It distinguishes itself from sibling tools like get_latest_news and get_news_recap by clarifying it returns category codes used by those tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool: before fetching news or recaps, as it returns codes needed by other tools. It does not explicitly state when not to use it or list alternatives, but the integration with sibling tools is well-explained.
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?
Given no annotations, the description fully carries the burden of behavioral disclosure. It clearly indicates that this is a paid endpoint, describes the return data structure (long/short context, entity analysis), and explains payment handling via USDC on Base network. No behavioral traits are hidden.
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 clear sections explaining the premium nature, return fields, and payment mechanism. It avoids fluff but could be slightly more streamlined by removing redundant phrasing like 'full entity analysis and token mentions' which is already implied by 'complete news data'.
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 zero-parameter tool with no output schema and no annotations, the description adequately explains the tool's premium nature, return structure, and payment details. It is complete enough for an agent to understand when to invoke it, though it could mention any rate limits or authentication requirements.
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 parameters and is 100% covered (no params to cover). The description adds value by explaining what the tool returns, but there are no parameters to elaborate on. Baseline 3 is appropriate as the description does not need to add parameter meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves enriched news with AI-generated context and analysis, differentiating it from siblings like 'get_latest_news' and 'get_news_item' by highlighting the premium AI context and payment requirement.
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 this tool (for enriched AI context) but does not explicitly state when not to use it or name alternatives. It mentions payment via x402 but lacks guidance on prerequisites or comparison with sibling tools.
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?
With no annotations, the description carries full burden for behavioral disclosure. It states the tool returns 'real-time' news with specific fields and is 'curated'. It does not mention authentication, rate limits, ordering, or what happens when no news is available. The description adds some value beyond the schema but lacks depth on behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized, with the main purpose front-loaded. The Args section is structured and readable. One minor inefficiency: the category documentation appears twice (in prose and in Args), but overall it is concise and well-organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown), the description is not required to detail return values. It mentions headline, sentiment, categories, sources. Two parameters are fully explained. It covers the main use case (getting latest filtered news) but omits edge cases (e.g., empty results, error handling) and does not clarify ordering ('latest' is vague but acceptable for a simple list tool).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds clear semantic meaning: category filter with examples (bitcoin, defi, ai), limit range (1-10) with default (5), and effect of omitting category. This goes well beyond what the schema provides (only type/default).
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 the latest curated crypto news headlines' with specific return fields (headline, sentiment, categories, sources). It distinguishes itself from sibling tools like get_categories by recommending calling get_categories first, and from get_news_item/get_enriched_news by implication (latest headlines vs detailed items).
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 using the category parameter and advises calling get_categories to see available codes. It explains when to omit category for all news. However, it does not explicitly contrast usage with other siblings like get_enriched_news or get_news_recap, so guidance on alternative tools is limited.
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 mentions 'full free-tier details' and lists what is returned (headline, sentiment, categories, sources, tweet URL), which provides useful behavioral transparency. However, it does not mention whether the resource is read-only, any rate limits, or what happens if the ID is invalid.
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 with the core purpose. The additional detail about return fields and parameter source is relevant and efficient. However, the inclusion of the 'Args:' docstring is slightly redundant given the input schema, though it adds helpful context.
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 low complexity (single parameter, no output schema, no annotations), the description provides sufficient context: what the tool does, what it returns, and where the ID comes from. It is complete enough for an agent to invoke correctly, though missing some behavioral details (error handling, rate limits).
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 0%, so the description must compensate. The description explains the 'id' parameter's source (from get_latest_news or search_news results), which adds significant meaning beyond the schema's minimal type annotation. This gives the agent necessary context to supply a valid ID.
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 specific news item by its ID', which is a specific verb-resource combination. It distinguishes from siblings like 'get_latest_news' and 'search_news' by focusing on retrieving a single item by ID, which is unique among the sibling tools.
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 context by noting the ID comes from get_latest_news or search_news, which provides guidance on when to use this tool. However, it lacks an explicit statement of when not to use it or mention of alternatives for other use cases.
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?
No annotations are provided, so the description carries the full burden. It discloses that the recap is AI-generated, returns a narrative, and mentions update frequency for certain categories (hourly for crypto/macro). However, it does not disclose potential rate limits, authorization needs, or what happens on invalid input.
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 the purpose, and uses a clear 'Args' section for parameters. Every sentence adds value, with 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 no output schema, the description covers parameters well and adds behavioral hints (update frequency). It could be improved by explicitly describing the return format (e.g., 'Returns a plain text summary') to compensate for the missing output schema.
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 0%, but the description compensates by explaining each parameter in detail: category as required code with examples and a pointer to get_categories, timeframe with recommended durations per category type and default value. This adds significant meaning beyond the raw 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 verb 'Get' and resource 'AI-generated news recap/summary for a specific category.' It distinguishes itself from sibling tools like get_latest_news (individual articles) and search_news (search) by specifying it returns a concise narrative summary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a general use case ('Great for getting up to speed quickly') and mentions using get_categories to see options, but does not explicitly state when to use this tool versus its siblings (e.g., when to use get_enriched_news instead). Usage guidance is implied but not explicit.
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 provided, the description must carry the full burden. It discloses that the tool is paid via x402 (USDC on Base), returns decision-grade bullet points, and provides payment instructions. It does not contradict any annotations (none exist). A small gap is the lack of explicit mention that it is a read-only operation, but the output description implies no side effects.
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 five sentences, front-loaded with the core purpose. Each sentence adds information (purpose, output style, audience, payment mechanism, what the tool returns). It is concise but could be slightly tighter by combining the payment and return instructions. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool having no parameters or output schema, the description is incomplete. It fails to explain how the agent specifies the ticker/topic (the schema is empty, so the input mechanism is unclear). Additionally, the description is ambiguous: it first says 'returns decision-grade bullet points', then later says 'returns the payment endpoint and instructions'. This contradiction undermines the agent's ability to know what the tool actually returns. Given the tool's simplicity, the description should clarify the input method and resolve the output ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so the baseline is 4. The description adds value by clarifying that the tool accepts 'any crypto ticker or topic' (even though no parameter exists in the schema), which helps the agent understand the conceptual input. No parameter details are needed beyond this.
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 verb ('Get'), resource ('24-hour AI-generated summary'), and scope ('any crypto ticker or topic'). It also distinguishes from sibling news tools by focusing on AI-generated summaries rather than raw news articles or categories.
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 the tool is for obtaining concise summaries and targets fund managers/trading agents, but it does not explicitly compare to sibling tools like get_enriched_news or get_latest_news, nor does it provide when-not-to-use guidance or exclusions. Usage context is present but not prescriptive.
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?
No annotations are provided, so the description needs to compensate for behavioral gaps. It clarifies that searches are across 'all news items for matching content' and lists return fields, but fails to disclose if results are real-time, cached, or have any rate limits. The tool is likely read-only, but this is implied rather than stated.
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 (4 lines of prose) and front-loaded with the core purpose. The docstring-style parameter list is efficient and adds necessary detail without redundancy. Every sentence serves a clear purpose.
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 (2 parameters, no nested objects), the description covers the basic usage and output fields well. However, absence of any annotations or output schema details (despite the context signal indicating an output schema exists) leaves users uncertain about the result structure beyond what's listed. The context also lacks sibling differentiation beyond the basic purpose.
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 description coverage is 0%, the description compensates well by explicitly listing the two parameters with examples ('e.g. 'ETF', 'SEC', 'Uniswap' for query) and a clear constraint (limit: 1-5, default 5). The examples and range add meaning beyond the basic schema fields.
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 ('Search') and resource ('curated crypto news by keyword'), immediately clarifying the tool's scope. It distinguishes itself from sibling tools like get_latest_news (which likely returns recent articles without search) and get_news_item (which fetches a single item 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 usage for keyword-based search and explains return fields (headlines, sentiment, categories, sources), but does not explicitly state when not to use it or suggest alternatives like get_enriched_news for more detailed data or get_news_recap for summaries.
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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