NewzAI News MCP server

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions that language is not a filter and news may include other languages, adding some context. However, it doesn't cover critical aspects like rate limits, authentication needs, pagination, or error handling, leaving significant gaps for a tool with multiple parameters.

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 two sentences, front-loaded with the core purpose. The note about language adds necessary clarification without redundancy. However, the first sentence is slightly verbose with the list of fields, which could be streamlined.

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, 100% schema coverage, and presence of an output schema, the description is reasonably complete. It covers the purpose and key behavioral note about language. With output schema handling return values, the description doesn't need to explain outputs, making it adequate for the 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 description coverage is 100%, so the schema fully documents all parameters. The description adds minimal value by reiterating that language is not a filter and specifying output language, but doesn't provide additional meaning beyond what's in the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the action ('fetches news') and resource ('for a predefined category'), specifying the detailed fields returned. It distinguishes from 'get_news_headlines' by mentioning 'full details' and from 'search_news' by focusing on categories rather than search queries. However, it doesn't explicitly name these siblings for differentiation.

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

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

The description implies usage by specifying inputs like category, region, and language, but doesn't explicitly state when to use this tool versus alternatives like 'get_news_headlines' or 'search_news'. It provides some context with the note about language not being a filter, but lacks clear when/when-not 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?

With no annotations provided, the description carries the full burden and adds valuable behavioral context: it discloses that login is required (an auth need) and explains the dependency on get_user_preferences for obtaining IDs. However, it lacks details on rate limits, pagination, or error handling.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by two concise sentences adding critical usage and behavioral context, with zero wasted words.

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

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

Given the tool's moderate complexity, no annotations, and an output schema (which handles return values), the description is mostly complete: it covers purpose, usage prerequisites, and auth needs. However, it could improve by mentioning potential limitations like result count or time ranges.

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 the parameter. The description adds minimal value by mentioning that the preference defines 'category, region, and language of news to retrieve,' but this doesn't provide syntax or format details beyond the schema's 'ID of the saved user preference.'

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

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

The description clearly states the specific action ('fetches news') and resource ('for a specific saved user preference identified by its ID'), distinguishing it from siblings like get_news_by_category, get_news_headlines, and search_news by emphasizing the preference-based retrieval mechanism.

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 explicitly provides when-to-use guidance by stating 'Use get_user_preferences first to obtain valid preference IDs' and 'Login is required to access this tool,' clearly differentiating it from alternatives that might not require login or use different parameters.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the output format ('title and source only, no summary') and regional filtering, but does not mention rate limits, authentication needs, pagination, or error handling. It adds some context but lacks comprehensive behavioral details.

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 zero waste. The first sentence states the purpose and key limitation, and the second provides explicit usage guidance. Every word earns its place, and information is front-loaded appropriately.

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 that an output schema exists (so return values need not be explained), the description covers purpose, limitations, and usage guidelines well. However, as a tool with no annotations, it could benefit from more behavioral context like rate limits or error handling. It is largely complete but has minor 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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as explaining the relationship between region and language or clarifying edge cases. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description clearly states the specific action ('fetches'), resource ('latest headlines'), and scope ('title and source only, no summary'), distinguishing it from sibling tools. It explicitly mentions what it does not provide (full article details), 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 states when to use this tool ('for a specified region') and when to use alternatives ('Use get_news_by_category or search_news when full article details... are needed'). It provides clear guidance on tool selection based on output needs.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively adds context beyond the input schema by explaining the two modes of operation (by_id vs. by_category), detailing the return structure ('related_news' and 'close_news' with specific fields), and stating authentication requirements ('Login is required'). It does not cover aspects like rate limits or error handling, but provides substantial behavioral insight.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose, followed by parameter guidance, return details, and authentication note. Every sentence earns its place by providing essential information without redundancy, making it efficient and well-structured.

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

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

Given the complexity of a 5-parameter tool with no annotations but a detailed input schema and an output schema (implied by 'Returns a dict'), the description is complete enough. It covers purpose, usage modes, return structure, and authentication, addressing key contextual needs without needing to explain return values in detail due to the 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?

The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning that at least one of 'by_id' or 'by_category' is required and explaining the difference in retrieval logic. This meets the baseline for high schema coverage without significant additional 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 the tool's purpose with specific verbs ('fetches', 'retrieved') and resources ('news related to a given topic or a specific news item'). It distinguishes from siblings by focusing on relatedness rather than direct retrieval or user preferences, unlike tools like 'get_news_by_category' or 'search_news'.

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 this tool ('Provide either a news item ID or a free-form category/topic string') and distinguishes between the two modes. However, it does not explicitly state when NOT to use it or name specific alternatives among the sibling tools, such as using 'search_news' for broader queries.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a read operation ('Returns'), requires authentication ('Login is required'), and specifies the data structure returned. However, it lacks details on error handling, rate limits, or response format beyond the preference fields listed.

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 in the first sentence, followed by essential details in a second sentence. Every sentence adds value: the first defines the action and scope, the second specifies the data structure, and the third states authentication requirements, with zero wasted words.

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

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

Given the tool's low complexity (0 parameters, no annotations, but with an output schema), the description is mostly complete. It covers purpose, data returned, and authentication needs. However, it could benefit from mentioning the output schema's role or potential limitations, though the presence of an output schema reduces the need for detailed return value explanations.

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 0 parameters with 100% coverage, so the description has no parameters to document. It appropriately focuses on the tool's purpose and behavior without redundant parameter information, earning a baseline score above 3 for compensating with clear context in a parameter-less 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 states the specific action ('Returns all saved news preferences') and resource ('for the authenticated user'), with explicit details about what each preference contains. It distinguishes this tool from siblings like 'get_news_by_category' or 'set_user_preferences' by focusing on user preferences rather than news content.

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 this tool ('Login is required to access this tool'), but it does not explicitly state when not to use it or name specific alternatives. It implies usage for retrieving saved preferences rather than news data, which helps differentiate from sibling tools.

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

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

With no annotations provided, the description carries full burden and adds valuable behavioral context: it clarifies that language is for output only (not a filter), mentions that news in other languages may be included, and describes the return format ('full details including summary, age, and source URL'). However, it doesn't address rate limits, authentication needs, or error conditions.

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 two sentences: the first states purpose and return format, the second clarifies parameter usage. It's front-loaded with core functionality. Minor room for improvement in flow, but overall efficient with no wasted words.

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

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

Given 7 parameters, 100% schema coverage, and an output schema (implied by context signals), the description provides adequate context for a search tool. It covers key behavioral aspects (language handling, return format) but could better address when to use versus siblings or advanced search behaviors. The presence of output schema reduces need to describe return values.

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 thoroughly. The description adds minimal semantic value beyond the schema—it mentions region filter, output language, and top_k but doesn't provide additional context or examples not already in parameter descriptions. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states the tool's purpose with specific verbs ('searches news') and resources ('full details including summary, age, and source URL'). It distinguishes from sibling tools (get_news_by_category, get_news_headlines) by emphasizing free-form topic/keyword search rather than categorical or headline-based 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?

The description implies usage through its parameter explanations (e.g., 'Specify the region filter, output language, and top_k') but doesn't explicitly state when to use this tool versus alternatives like get_news_by_category or get_news_headlines. No clear exclusions or comparative guidance is provided.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the mutation behavior ('Adds... preferences'), clarifies that it's an append operation rather than a replacement, and states the authentication requirement ('Login is required'). It doesn't mention rate limits, error conditions, or response format, but covers the essential behavioral traits for this type of 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 perfectly sized and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds essential information about the operation's behavior and requirements without any wasted words. The three sentences each earn their place by providing 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?

Given that there's an output schema (though not shown here), the description doesn't need to explain return values. It covers the mutation nature, authentication requirement, and append behavior well. For a tool with 100% schema coverage and output schema, the description provides adequate context, though it could potentially mention success/failure indicators.

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 description coverage is 100%, so the schema already fully documents the single parameter and its nested structure. The description mentions what each preference contains but doesn't add syntax or format details beyond what the schema provides. This meets the baseline expectation when schema coverage is complete.

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

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

The description clearly states the specific action ('Adds one or more news preferences'), the resource ('for the authenticated user'), and the scope ('Each preference specifies a news category, region, output language, and a daily refresh time'). It explicitly distinguishes this from sibling tools like 'get_user_preferences' by focusing on addition rather than 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?

The description provides clear context for when to use this tool ('Login is required to access this tool') and clarifies the behavior ('Existing preferences are not removed — new ones are appended'), which helps differentiate it from potential alternatives like update or replace operations. However, it doesn't explicitly name when-not-to-use scenarios or mention specific sibling alternatives.

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