revieworacle

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

With no annotations, the description alone must disclose behavioral traits. It mentions the scope (negative news, recalls, warnings, lawsuits) but omits important details such as output format, data sources, freshness of results, or whether it's a read-only operation. The description is too sparse to provide adequate transparency.

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, front-loaded sentence that directly states the tool's purpose. Every word contributes to understanding, with no redundant or irrelevant 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?

The tool has no annotations and no output schema, so the description must explain enough about behavior and return values. It fails to mention what the result looks like, how many alerts are returned, or any pagination/aggregation details. Given the minimal description and absence of structured context, the completeness is inadequate for a tool that likely returns a list of alerts.

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 parameters 'lang' and 'brand' are already well-documented in the schema. The description adds context that the brand is checked for negative alerts, but it doesn't clarify that 'brand' is functionally required despite having no required flag in the schema. Overall, the description adds minimal value 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 checks for recent negative news, recalls, warnings, or lawsuits about a brand, which gives a specific verb and resource. It distinguishes itself from sibling tools like review_search or sentiment_trend by focusing on negative alerts, though it doesn't explicitly name alternatives.

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 (to check for negative brand alerts) but provides no explicit guidance on when not to use it or how it differs from similar tools like brand_monitor or health_check. No alternatives or exclusions are mentioned.

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 discloses the return structure (mention count, top sources, recent articles) but omits whether the operation is read-only, any authentication requirements, rate limits, or what 'monitor' implies in terms of ongoing tracking vs. a one-time snapshot. The behavior is only partially transparent.

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 core purpose and followed by concrete return outputs. Every word earns its place with no filler or repetition of schema details. It is efficiently written for quick comprehension.

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 (2 optional parameters, no output schema) and the description covers purpose and return values. However, it lacks usage context, such as when to prefer this over sibling tools, or any caveats about language defaults or data freshness. For a tool with no annotations, it is minimally complete but leaves 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 coverage is 100%, so both parameters (lang, brand) are already documented in the input schema. The description adds no additional meaning about how to use these parameters, such as language constraints or expected brand name formats. It merely repeats the general purpose, so the baseline score of 3 applies.

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

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

The description clearly states a specific action ('Monitor') and resource ('all news mentions of a brand'), and lists concrete return values (mention count, top sources, recent articles). It distinguishes itself from siblings like product_reviews and review_search by explicitly focusing on news mentions rather than general reviews, and from competitor_compare by being brand-specific.

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 use when news mentions of a brand are needed, but does not explicitly state when to use this tool versus alternatives like sentiment_trend or alert_check. There is no mention of exclusions or alternative tools, so guidance remains implicit rather than explicit.

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?

No annotations are present, so the description carries the full burden. It states the tool compares sentiment and returns scores for both, which implies a read-only analysis. However, it omits details like scoring scale, date range, or filtering behavior, leaving some ambiguity.

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 sentence that immediately states the action and output. There is no redundancy or filler; every word contributes value.

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

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

The tool is relatively simple, but with no output schema and no annotations, the description only partially covers return semantics. It doesn't mention the sentiment score range, whether brand/competitor are effectively required despite being optional in the schema, or how the language parameter affects results.

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 three parameters are covered by the schema at 100%, and the description adds no extra parameter-level meaning. It doesn't explain score format or missing-parameter behavior, but the schema already documents names and defaults, so the baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Compare') and identifies the resource ('news sentiment') and the comparison target (brand vs competitor). This clearly distinguishes it from siblings like sentiment_trend, which would track a single entity.

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

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

The description gives clear context: use when comparing brand sentiment against a competitor. It doesn't explicitly mention alternatives or exclusions, but the 'between your brand and a competitor' framing is enough to indicate primary usage.

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?

No annotations are present, so the description is the only source of behavioral information. It implies a read-only health check but does not explain what 'status' entails (e.g., uptime, latency, response format), whether it might fail, or any side effects. This is minimal disclosure.

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 extremely short ('ReviewOracle server status.'), which is concise, but it is a fragment rather than a complete sentence. It lacks any additional detail that would make the tool's purpose or behavior clearer, so it is under-specified despite being terse.

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

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

With no output schema and minimal description, the tool is insufficiently documented. It does not explain what a 'status' result looks like, what information it contains, or how to interpret it. For a simple health check, some context about expected outcomes is necessary.

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 the schema fully conveys this with an empty properties object. The baseline for no parameters is 4, and there is nothing to explain beyond that.

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 mentions the resource ('ReviewOracle server') and implies the action ('status' suggests checking), but it lacks an explicit verb like 'checks' or 'returns'. It does not differentiate from sibling tools such as alert_check or brand_monitor, though those names suggest different purposes.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, typical scenarios, or situations where a different tool would be more appropriate.

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, the description carries the full burden of behavioral disclosure. It mentions a source preference but doesn't reveal ordering, filtering behavior, language handling, pagination, or any limitations. This is a significant gap for a search tool with no output schema or annotation hints.

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, focused sentence that immediately communicates the tool's purpose and a key differentiator. Every word adds value; there is no filler or redundancy.

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

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

The tool has three optional parameters, no output schema, and no annotations, so the description must cover more ground. It explains what the tool does but not what it returns, how it handles empty input, or how it interacts with sibling tools. This leaves substantial ambiguity for an agent deciding when and how to invoke it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so all parameters are already described in the schema. The description adds only the trusted-source context, which indirectly guides parameter usage (e.g., searching for a product name). It does not add deeper semantics like interaction between category and product or default behavior when no parameters are provided.

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: finding product test and review articles. It also mentions a differentiator by highlighting trusted sources (Stiftung Warentest, CHIP, IMTEST). However, it doesn't explicitly distinguish itself from closely named siblings like 'review_search' or 'warentest_search'.

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?

There is no guidance on when to use this tool versus its siblings (e.g., review_search or warentest_search). The description implies it is for product test/review articles with trusted sources, but it doesn't provide explicit use cases, exclusions, or alternatives.

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?

No annotations are provided, so the description carries the transparency burden. It discloses that the tool returns articles with sentiment analysis, which is useful output behavior, but it does not mention pagination, sorting, rate limits, or other potential 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 concise sentences, front-loaded with the core action and scope, followed by the return type. Every sentence earns its place with no redundant or verbose 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 search tool with no output schema and no annotations, the description covers the basic purpose and return content. However, it lacks guidance on parameter relationships (e.g., query vs brand) and sibling differentiation, leaving some contextual 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?

The input schema has 100% description coverage, with clear explanations for lang, brand, limit, and query. The tool description adds no additional parameter semantics, so the baseline score of 3 is appropriate.

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

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

The description clearly states the tool searches for product reviews, tests, and ratings from news sources and returns articles with sentiment analysis. However, it does not explicitly differentiate from the sibling tool product_reviews, which likely has overlapping functionality.

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 domain context (news sources, product reviews, sentiment analysis), implying when to use it, but it gives no explicit guidance on when not to use it or how it compares to siblings like product_reviews and sentiment_trend.

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 present, the description carries the full burden for behavioral disclosure. It only states the output is a 'positive/negative/neutral breakdown' and that it uses 'recent news', but does not explain what 'recent' means, the data source, or any limitations such as language coverage beyond the parameter default.

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, tightly written sentence that front-loads the action and result. Every word contributes to understanding the tool's purpose and output, with zero filler or redundancy.

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

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

The tool is simple (two params, no nested objects, no output schema) and the description covers the core behavior and output. However, it omits specifics like the time window for 'recent news' and does not clarify the meaning of a neutral breakdown, leaving some gaps for an agent that needs operational detail.

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 both 'lang' and 'brand' already documented with clear explanations. The description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate; it neither harms nor enhances parameter understanding.

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 the specific verb 'Analyze' followed by the clear resource 'overall sentiment trend for a brand based on recent news'. This makes the tool's function immediate and distinct from sibling tools like product_reviews or review_search, which focus on review content rather than news-based sentiment.

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 offers no guidance on when to choose this tool over siblings such as brand_monitor or competitor_compare. It implies news-based analysis but never states exclusions, prerequisites, or alternative recommendations, leaving the agent without decision criteria.

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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that the tool searches results, without mentioning any behavioral traits such as result limits, pagination, rate limits, authentication requirements, or whether the operation is read-only. This is a significant gap for a tool with no annotation support.

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, clear sentence that is front-loaded with the action and resource. It contains no redundant words and is appropriately concise for a simple search tool.

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

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

With no output schema and no annotations, the description should clarify what the search returns and in what form. It only says 'results' without describing whether it returns product names, ratings, prices, or links. Additionally, it lacks usage context relative to sibling tools and gives no hints about how to deconstruct the response. For a tool with one parameter, this is insufficient.

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 provides 100% coverage with a descriptive example for the 'query' parameter. The description adds minimal extra meaning ('any product category') but does not introduce new details or clarify why the parameter is optional (required parameters: 0). Schema does the heavy lifting, so a baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'Search' and the resource 'Stiftung Warentest results', and it qualifies the scope as 'any product category'. This distinguishes it from sibling tools like product_reviews or review_search by specifying the unique source (Stiftung Warentest).

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?

There is no explicit guidance on when to use this tool versus alternatives. Sibling tools like product_reviews and review_search may overlap in functionality, but the description does not mention them or provide exclusion criteria. The phrase 'for any product category' implies a broad use case, but the intended context relative to siblings is missing.

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