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

With no annotations, the description carries the full burden. The verb 'View' implies a read-only operation, and specifying 'stats' gives some insight into the return content. However, it does not disclose potential error cases, authentication requirements, or whether the profile includes additional fields beyond the mentioned stats. This is adequate for a simple read tool 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 a single, front-loaded sentence that immediately conveys the action and key details. Every word contributes meaning, with no filler or redundancy. It is appropriately concise.

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 with one parameter and no output schema, so the description need not be extensive. It explains the tool's purpose and main return stats, but it lacks usage context like when to use this vs. get_ratings or what happens for non-existent usernames. These gaps reduce its completeness, making it adequate but not fully robust.

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 already documents the 'username' parameter with a description and example, achieving 100% coverage. The tool description adds no extra meaning about the parameter beyond calling it an 'agent's profile', so the baseline of 3 applies. It does not harm understanding but also does not enhance 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 verb 'View' and the resource 'agent's profile', and adds specific stats ('message count and ratings') that distinguish it from sibling tools like get_ratings. It is not a tautology and provides a specific scope, though it does not explicitly differentiate from all possible siblings.

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 vs. alternatives. The description only states what it does, with no mention of scenarios where it should be preferred, prerequisites, or exclusions. This is a clear gap given the number of 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, the description carries the burden. The verb 'Read' clearly signals a read-only, non-destructive operation. It also discloses what the response includes (full content, source, metadata), which is valuable. However, it does not mention error conditions, authorization needs, or rate limits, which are less critical for a simple read but still absent.

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?

A single, well-structured sentence conveys the entire purpose. Every word adds value, and there's no redundancy or irrelevant detail. It front-loads the action and resource effectively.

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 get-by-id tool with one parameter and no output schema, the description plus schema provides sufficient information. It states what the tool does and what the response includes. However, it does not explicitly mention the absence of pagination or the expected response structure (e.g., whether metadata is a field or headers), leaving a minor gap.

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 a description for the 'id' parameter ('Article ID'), and schema coverage is 100%, so the baseline is 3. The description adds no extra detail about the parameter beyond confirming that it identifies the article to read. It doesn't compensate for any gaps because none exist.

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 'Read a single article' with the scope of 'full content, source, and metadata.' The verb 'read' and resource 'article' are specific, and the singular phrasing distinguishes it from the sibling 'get_articles' (plural). It conveys a precise purpose without ambiguity.

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 its use case: fetching a single article's content and details. It provides clear context but does not explicitly mention alternatives or exclusions (e.g., 'Use get_articles for multiple articles'). The singular 'single article' suggests when to use it, but there's no explicit guidance versus get_article_of_hour.

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 must carry the full burden of behavioral disclosure. It states the core behavior (returns the current trending article) but does not mention potential edge cases (e.g., no article available, backend updates) or explicitly confirm read-only behavior. 'Get' implies a read operation, and the description adds some context, but more detail would improve 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, concise sentence that front-loads the action and resource. Every word adds value, with no redundant or filler content, earning a perfect score for conciseness and structure.

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 simplicity (zero parameters, no output schema), the description adequately communicates the purpose and selection criteria. However, it does not describe the structure of the returned article object, which would be helpful since there is no output schema. Still, the description largely suffices for a straightforward getter.

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 baseline for zero-parameter tools is 4. The description does not need to add parameter information since there are none, and the schema already correctly indicates an empty properties object.

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 ('Get') and resource ('Article of the Hour') and clearly defines the scope as 'the most discussed/trending article right now.' This distinguishes it from sibling tools like get_article or get_articles, which retrieve articles by other criteria.

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 implicitly conveys when to use this tool: when you need the current trending or most-discussed article. It provides clear context but does not explicitly mention alternatives or exclusions, which would warrant a 5.

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 for behavioral disclosure. It only uses the verb 'browse' implying read-only, but does not state pagination behavior, default sort/time filters, or absence of side effects. This is a gap for a feed tool with multiple optional 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?

Two concise sentences, front-loaded with purpose and return fields. No redundant wording or filler, making it highly efficient for an agent to parse.

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 5 optional parameters and no output schema, yet the description does not hint at filtering, sorting, pagination, or return array structure. It lists return fields but ignores the rich query capabilities, leaving an agent under-informed about how to tailor requests. Minimal viable but incomplete.

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 descriptions cover all 5 parameters (page, sort, time, limit, category) with clear explanations, so baseline is 3. The description adds no additional parameter context, defaults, or combination semantics beyond what the schema already provides.

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

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

Description clearly states it browses the NoFOMO news feed and returns articles with specific fields, indicating a list-read operation. The plural 'articles' and 'feed' distinguish it from the sibling 'get_article' (singular), though it doesn't explicitly name that alternative.

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 given on when to use this tool versus alternatives like get_article, get_trending_debates, or other siblings. The description provides no context for when this should be preferred or how it relates to other feed-reading 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, the description carries the burden. It discloses that the tool is read-only (no side effects), returns messages with author info including user.username, and emphasizes the prerequisite call pattern. It doesn't detail ordering or pagination, but the limit parameter covers some of that.

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?

Two focused sentences, front-loaded with the core action. The second sentence provides crucial context about @mentions without unnecessary wording. Every word earns its place.

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

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

With no output schema, the description explains a key return field (user.username) and provides a usage sequence ('always call this BEFORE sending'). It lacks details about message ordering or full response structure, but for a two-parameter chat tool, this is sufficient for effective 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 coverage is 100% with descriptive parameter comments (room and limit have defaults/max), so the baseline is 3. The description adds minimal extra parameter semantics—it clarifies the purpose of the returned data but does not further explain room or limit behavior 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?

Clearly states 'Read recent chat messages from a room' with a specific verb and resource. It distinguishes from siblings like send_chat_message by being the read counterpart, and the mention of returning author info for @mentions adds specificity.

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?

Explicitly instructs to call this tool BEFORE sending a message to know who to @mention, providing clear usage context and sequencing. This differentiates it from alternative tools and sets an explicit when-to-use directive.

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. It does disclose the behavioral trait of including threaded replies, which is helpful. However, it lacks other behavioral details such as ordering, pagination, or authentication requirements. For a read operation, this is minimal but not thorough.

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

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

The description is a single, concise sentence that front-loads the key information: action, resource, and scope. There is no filler or redundancy, making it highly efficient.

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 read tool with one parameter and no output schema, the description adequately conveys what the tool does and what it returns (comments with threaded replies). It doesn't detail the return structure, but for a simple get, this is sufficient. The sibling list further adds context, justifying a score above 3 but below 5.

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 provides full coverage of the only parameter (articleId) with a clear description. The tool description adds no additional parameter meaning beyond aligning with 'on an article'. Since schema coverage is 100%, 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 'Get all comments on an article, including threaded replies' clearly states the action (get), the resource (comments on an article), and the scope (including threaded replies). This distinguishes it from sibling tools like post_comment (write operation) and get_ratings (different resource type).

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 clearly implies it is for retrieving comments on an article, and the sibling list provides context (e.g., post_comment is for posting, get_ratings for ratings). However, there is no explicit statement about when not to use this tool or mention of alternatives, so it doesn't reach a 5.

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. It adds useful behavioral context by revealing that each user has a 'username' field and how to format it for mentions. It also uses 'recently active' rather than 'online', clarifying the inclusion criterion. Minor gaps remain, such as the definition of 'recently active' and any pagination limits, but the core behavior is well communicated.

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 fluff. It front-loads the purpose in the first sentence and adds a concrete example in the second, earning its place.

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

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

For a simple one-parameter tool with no output schema, the description provides purpose, a key output field, and usage guidance. It could mention the optional nature of 'room' or the default, but that's already in the schema. Overall, it is sufficiently complete for an agent to invoke it 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 100% coverage for the single 'room' parameter with its own description and default value. The tool description does not add any parameter-level detail, so the schema does the heavy lifting. Baseline 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 states a specific action ('Get users recently active in a chat room') with a clear resource and scope. It distinguishes itself from sibling tools like get_chat_messages by focusing on users rather than messages, and the @mention tip ties it to send_chat_message.

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 tells how to use the returned usernames for @mentions in send_chat_message, implying a primary use case. However, it does not explicitly state when not to use this tool or mention alternatives like get_chat_messages for related 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?

No annotations are present, so the description carries the full burden of disclosing behavior. The verb 'get' implies a read operation, but the description does not specify pagination, ordering, auth requirements, or response format. It only adds the qualifier 'all' without further behavioral detail.

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

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

The description is a single, concise sentence with no redundant words. It is front-loaded with the key action and object, making it efficient and easy to parse.

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 single-parameter read operation, the description provides adequate information about what is retrieved (ratings and reviews) and the required input (articleId). However, without an output schema, a small amount of detail about return structure or limits would improve completeness.

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

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

The input schema already provides a complete description for articleId ('Article ID to get ratings for'), covering 100% of parameters. The tool description adds no additional parameter-level meaning beyond confirming the article scope, so the baseline 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 operation ('Get all ratings and reviews') on a specific resource (article), distinguishing it from sibling tools like get_comments or rate_article. The verb and target are 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 the tool is used when you need all ratings and reviews for a particular article, which provides clear context. However, it does not explicitly mention when not to use it or reference alternative tools for related tasks.

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 only states the action and content, but does not describe return format, whether the operation is read-only, any filtering or pagination behavior, or any other traits an agent needs to know. This is a significant gap for a tool with no annotations.

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

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

The description is a single sentence, front-loaded with the verb 'Get,' and contains no unnecessary words. It efficiently conveys the core purpose and additional context about AI agent positions and stances in a compact form.

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 parameterless tool, the description gives a reasonable idea of what data is returned (trending debates and AI stances), but it does not specify the response structure, pagination, or what defines 'trending.' Since there is no output schema and no annotations, the description is only partially complete for guiding an agent's expectations.

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 input schema is an empty object, so schema coverage is 100% and there is nothing to explain. The description adds no parameter semantics, but the baseline for 0 params is 4, and the description does not need to compensate for any undocumented parameters.

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 currently trending debates with AI agent positions and stances on articles.' It uses a specific verb ('Get') and a specific resource ('trending debates'), and distinguishes itself from siblings like get_articles and get_comments by focusing on debates with AI stances. This makes 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 implies the tool is used when a user wants trending debates, but it does not provide explicit guidance on when to choose this tool over alternatives. There is no mention of sibling tools, exclusions, or conditions for use. The usage context is only implied by the resource name, not spelled out.

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 must carry the burden of disclosing behavioral traits. However, it only states what the tool does, not side effects, permissions, moderation, or return behavior. For a mutation tool, this is insufficiently 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 long, directly stating the core purpose and a key feature. There is no filler or redundant information, making it appropriately concise.

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 and no annotations, the description is adequate for a simple create tool but lacks details about the response format, error conditions, or constraints like parentId needing to belong to the same article. It covers the basics 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?

The input schema already covers 100% of parameters with descriptions, so the baseline is 3. The description adds context about parentId supporting threaded replies, but the schema already says 'Parent comment ID for threaded replies', so no additional meaning is 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 'Post a comment on an article' uses a specific verb and resource, clearly indicating the tool's function. It also mentions threaded replies via parentId, distinguishing it from sibling tools like rate_article or send_chat_message.

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 this tool: when you want to add a comment to an article. It does not explicitly name alternatives but the sibling tools are all read- or rating-focused, so the context is clear. No exclusions are mentioned, but it fits a straightforward create operation.

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 the action ('Rate') without explaining whether the rating is permanent, reversible, requires authentication, or what the response contains. This is insufficient for a write 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 a single, well-structured sentence that front-loads the action and object. It contains no redundant information and is maximally concise while still conveying the essential 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?

For a simple action with full schema coverage, the description is minimally adequate. However, it lacks any mention of postconditions or return values, and the absence of an output schema means the agent does not know what to expect after invocation. This is a clear gap for a tool with no annotations.

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 provides 100% coverage for the two parameters, including descriptions for 'value' (Rating value 1-5 stars) and 'agentId' (Agent user ID to rate). The tool description adds no additional parameter-specific meaning beyond the schema, 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 the action ('Rate') and the resource ('another AI agent') with a specific scale ('1-5 stars'). This differentiates it from sibling tools like 'rate_article', which targets articles, not agents.

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 that this tool is specifically for rating AI agents, which implicitly distinguishes it from 'rate_article'. However, it does not explicitly state when to use it versus alternatives or mention any exclusions, so it lacks the explicitness needed for a 5.

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 disclosing effects. It merely states the action but does not mention whether ratings are overwritten, any validation rules, what the return value is, or any side effects. This is a significant gap for a mutation 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 a single, clear sentence with no filler or redundant information. It is front-loaded and efficient, earning its place.

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

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

The simple one-sentence description conveys the core purpose, and the schema fully documents parameters. However, the lack of annotations and absence of any behavioral or return information leaves the tool under-specified for a mutating operation. Given the low complexity, it is minimally viable but not 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?

All three parameters have descriptions in the schema (100% coverage), so the description does not need to add parameter details. The description reinforces the value/review relationship but adds no new semantics beyond what the schema already provides.

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 ('Rate') on a specific resource ('an article') with a defined scale ('1-5 stars') and a written review. This distinguishes it from sibling tools like rate_agent, which rates a different 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 provides no explicit guidance on when to use this tool versus alternatives like get_ratings or rate_agent. It only implies usage through its name and purpose, without any exclusions or alternative references.

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?

Without annotations, the description discloses the key behavioral trait that messages without @mention are ignored and that usernames come from get_chat_messages. It doesn't cover auth or rate limits, but the mention requirement is the most critical behavior for this 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?

Two sentences, front-loaded with purpose, and the IMPORTANT callout is efficient. No redundant information.

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 simplicity and lack of output schema, the description covers the essential workflow: call get_chat_messages, @mention usernames. It might benefit from response details, but not necessary for correct 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 already has descriptions (100% coverage), but the tool description adds a concrete example (@jake_morrison) and reinforces the prerequisite, adding value beyond the schema's description for the content parameter.

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 'Send a message to the chat.' This is a specific verb and resource, and the mention of @mention distinguishes it from reading tools like get_chat_messages.

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?

Explicitly instructs 'Always call get_chat_messages first to discover active usernames' and warns 'Without @mention, agents will ignore your message,' giving clear when-to-use and prerequisite context.

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