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 adds context by stating authentication requirements and hinting at mutation (since it's adding/replying), which is useful. However, it lacks details on rate limits, error conditions, or what happens on success (e.g., does it return the new comment ID?). The description compensates partially but leaves gaps 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 extremely concise with two sentences that efficiently convey the core action, scope, and a key requirement. Every word earns its place, and it's front-loaded with the primary purpose. There's no redundancy or unnecessary elaboration.

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 (a mutation with authentication), no annotations, and no output schema, the description is somewhat complete but has gaps. It covers authentication and basic usage but lacks details on behavioral traits like rate limits or return values. For a mutation tool without structured support, it should do more to be fully adequate.

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 three parameters thoroughly. The description adds minimal value beyond the schema by implying the optionality of 'replyToCommentId' through the phrase 'or reply to an existing comment', but doesn't provide additional syntax or format details. This meets the baseline for 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 ('Add a comment', 'reply to') and identifies the resource ('Instagram post' or 'existing comment'). It distinguishes from siblings like 'instagram_like_comment' or 'instagram_get_post_comments' by focusing on creation rather than retrieval or liking. However, it doesn't explicitly differentiate from all siblings (e.g., 'instagram_like_post' is also a post interaction).

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 mentioning authentication requirements and the optional nature of replying, but doesn't explicitly state when to use this tool versus alternatives. For instance, it doesn't clarify if this should be used over 'instagram_like_comment' for engagement or specify prerequisites beyond authentication. The guidance is present but not comprehensive.

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 clearly indicates this is part of a login flow and has a dependency on another tool, which is valuable context. However, it doesn't describe what happens after successful 2FA completion (e.g., session establishment, error handling for invalid codes, or rate limiting considerations), leaving some behavioral aspects unspecified.

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 concise with two sentences that each serve a distinct purpose: the first states what the tool does, and the second provides essential usage guidance. There's no wasted language, and the information is front-loaded with the core purpose immediately clear.

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

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

For a single-parameter tool with full schema coverage but no annotations or output schema, the description provides good contextual completeness. It establishes the tool's role in the authentication workflow and its dependency on another tool. The main gap is the lack of information about what happens after successful 2FA completion, but given the tool's focused purpose, the description is reasonably complete.

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

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

With 100% schema description coverage, the input schema already fully documents the single parameter. The description doesn't add any additional parameter semantics beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value. The description focuses on tool usage rather than parameter details.

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 ('complete Instagram login by providing the 2FA verification code') and resource ('Instagram login'), and explicitly distinguishes it from its sibling 'instagram_login' by indicating it should be used after that tool shows 2FA is required. This provides precise differentiation from other authentication-related 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 explicit guidance on when to use this tool ('after "instagram_login" indicates 2FA is required'), creating a clear dependency relationship. It also implicitly suggests when not to use it (when 2FA hasn't been triggered), though it doesn't name alternative tools for other authentication scenarios, which is appropriate given the specific 2FA context.

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 schema by stating 'Requires authentication' (auth needs) and 'May require approval for private accounts' (behavioral trait), which are crucial for understanding how the tool operates. However, it does not cover aspects like rate limits 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 front-loaded with the core purpose in the first sentence, followed by important behavioral notes. Every sentence earns its place by providing essential information without waste, making it highly 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 tool's complexity (a write operation with authentication and conditional behavior), no annotations, and no output schema, the description does a good job covering key aspects like purpose, auth needs, and private account handling. However, it lacks details on return values or error cases, leaving some gaps in 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 schema description coverage is 100%, with the parameter 'userId' fully documented in the schema. The description does not add any additional meaning or details about the parameter beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate as the schema handles the parameter documentation.

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 ('Follow an Instagram user') and resource ('by user ID'), distinguishing it from sibling tools like 'instagram_like_post' or 'instagram_comment_on_post'. It precisely communicates the tool's function without redundancy.

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 it ('Follow an Instagram user') and mentions a key condition ('May require approval for private accounts'), but does not explicitly state when not to use it or name alternatives among siblings. This gives good guidance but lacks explicit exclusions.

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 what the tool returns ('complete account details including username, full name, bio, follower/following counts, verification status, profile picture, account type, and settings') and discloses an important behavioral requirement ('Requires authentication'), which is crucial context not available elsewhere.

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 concise and front-loaded: the first sentence states the core purpose, the second elaborates on what's returned, and the third provides the authentication requirement. Every sentence earns its place 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 the tool's simplicity (0 parameters, no output schema, no annotations), the description provides good completeness by explaining what information is returned and the authentication requirement. However, without an output schema, it could benefit from more detail about the return format or structure.

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 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, which is correct for this case.

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 verb ('Get') and resource ('authenticated user's own profile information'), and distinguishes it from sibling tools like 'instagram_get_user_profile' by specifying it's for the authenticated user's own profile rather than any user's profile.

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 ('Get the authenticated user's own profile information'), but doesn't explicitly state when not to use it or name alternatives. It implies usage for the authenticated user's profile vs. other user profiles, but lacks explicit exclusions.

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 the return format ('paginated list of comments with author info, text, timestamps, and engagement metrics'), pagination mechanism ('Supports pagination via maxId cursor'), and authentication requirement ('Requires authentication'). This covers key operational traits beyond basic functionality.

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, followed by key details on returns, pagination, and authentication in three efficient sentences. Each sentence adds essential information without redundancy, making it highly concise 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 no annotations and no output schema, the description compensates well by explaining the return format, pagination, and auth needs. However, it lacks details on error handling, rate limits, or specific engagement metrics, leaving minor gaps for a read operation with 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?

Schema description coverage is 100%, so the schema already documents all parameters (mediaId, maxId, limit) thoroughly. The description adds no additional parameter-specific details beyond what the schema provides, such as examples or edge cases, meeting the baseline for 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 specific action ('Get comments') and resource ('for an Instagram post'), distinguishing it from sibling tools like 'instagram_get_post_details' (which likely provides different post metadata) or 'instagram_comment_on_post' (which creates comments). It precisely defines the tool's function 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 explicitly states 'Requires authentication,' providing clear context for when to use this tool (i.e., after login). However, it does not differentiate when to use this versus alternatives like 'instagram_get_post_details' (which might include comments but with different scope) or specify exclusions (e.g., not for stories or user profiles), missing explicit sibling comparisons.

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 return content (e.g., caption, metrics, URLs) but lacks details on permissions, rate limits, authentication needs, or potential errors. For a read operation in a social media API context, this omission is significant, though it does not contradict any 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 front-loaded and efficiently structured in two sentences: the first states the action and resource, and the second enumerates the return details. Every sentence adds value without redundancy, making it highly concise 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?

Given the tool's complexity (a read operation with no output schema and no annotations), the description is adequate but incomplete. It covers the purpose and return content well, but lacks behavioral context like authentication requirements or error handling. Without annotations or output schema, more detail would improve completeness for safe agent use.

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

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

The input schema has 100% description coverage, with the parameter 'mediaId' clearly documented. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 a specific verb ('Get detailed information') and resource ('Instagram post by media ID'), distinguishing it from siblings like instagram_get_post_comments (which focuses on comments) or instagram_get_user_posts (which lists posts). It specifies the exact scope of information retrieved, 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 implies usage by specifying 'by media ID,' which suggests it's for retrieving details of a known post, but it does not explicitly state when to use this tool versus alternatives like instagram_get_user_posts (for listing posts) or instagram_get_post_comments (for comments). No exclusions or prerequisites are mentioned, leaving some ambiguity in context.

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 behaviors: it's a read operation (implied by 'Get'), returns paginated results with specific content types (media URLs, captions, metrics, metadata), supports pagination via maxId, and requires authentication. This covers most essential aspects for a read-only API call, though it doesn't mention rate limits 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 efficiently structured in three sentences that each earn their place: first states purpose and scope, second details return format and pagination, third specifies authentication requirement. No wasted words, front-loaded with the core functionality.

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 read-only tool with 2 parameters and no output schema, the description provides good coverage: purpose, return format, pagination behavior, and authentication requirement. It doesn't describe the exact structure of returned posts or error cases, but given the tool's moderate complexity and lack of annotations, this is reasonably complete. The absence of an output schema means some return value details are missing.

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 fully documents both parameters (maxId for pagination cursor, limit with default and max). The description mentions pagination via maxId cursor but adds no additional semantic information beyond what's in the schema descriptions. This meets the baseline of 3 when schema coverage is high.

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 ('Get home feed'), the resource ('posts from accounts you follow'), and distinguishes it from siblings like instagram_get_user_posts (which gets posts from a specific user) and instagram_get_post_details (which gets details for a single post). The verb+resource+scope combination is precise and 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 provides clear context for when to use this tool ('Get home feed (posts from accounts you follow)'), which implicitly distinguishes it from sibling tools that fetch user-specific posts or search results. However, it doesn't explicitly state when NOT to use it or name specific alternatives, keeping it at a 4 rather than 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 behavioral disclosure. It adds useful context about pagination behavior ('Supports pagination via maxId cursor') and return content ('Returns posts with media URLs, captions, engagement metrics, and metadata'), but does not cover aspects like rate limits, authentication needs, or error handling, leaving gaps for a mutation-free read 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 appropriately sized and front-loaded, with two sentences that efficiently convey the tool's purpose and key behavioral traits (pagination and return content). Every sentence earns its place without redundancy or waste.

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 (read operation with pagination), no annotations, and no output schema, the description is mostly complete. It covers the purpose, pagination behavior, and return content, but lacks details on authentication requirements, rate limits, or error responses, which could enhance completeness for a tool interacting with an external API.

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 value beyond the schema by mentioning pagination via maxId cursor, but does not provide additional syntax, format details, or clarification for parameters like userId/username exclusivity. 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 specific action ('Get paginated feed'), resource ('user's Instagram posts'), and scope ('paginated feed'). It distinguishes from siblings like instagram_get_post_details (single post) and instagram_get_timeline_feed (timeline vs user posts) by specifying it retrieves a user's posts feed with pagination.

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 ('Get paginated feed of a user's Instagram posts') and implies usage through the mention of pagination. However, it does not explicitly state when not to use it or name alternatives (e.g., vs instagram_get_user_profile for profile info), which prevents a perfect score.

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 mentions the return data but does not disclose behavioral traits such as rate limits, authentication needs, error handling, or whether it's a read-only operation. This is a significant gap for a tool with no annotation coverage.

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 and efficiently lists key return details. Every sentence adds value without redundancy, making it appropriately sized 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?

Given no annotations and no output schema, the description provides basic purpose and return details but lacks completeness for a tool with potential complexities like authentication or rate limits. It covers the 'what' but not the 'how' or constraints, leaving gaps in 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 already documents both parameters (userId and username) and their constraints. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or usage tips, meeting the baseline for high 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 verb 'Get' and resource 'comprehensive Instagram profile information', specifying it retrieves details like username, bio, follower counts, etc. It distinguishes from siblings by focusing on profile data rather than posts, comments, or actions like following/liking.

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 retrieving profile info but does not explicitly state when to use this tool vs. alternatives like 'instagram_get_current_user_profile' or 'instagram_search_accounts'. No exclusions or prerequisites are mentioned, leaving some ambiguity.

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 (implied by 'Get'), specifies that stories expire after 24 hours (important context), and notes viewer count availability only for own stories. It lacks details on rate limits, authentication needs, or error handling, but covers essential behavior beyond basic purpose.

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 return details and expiration note. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.

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 annotations and no output schema, the description does a good job covering the tool's behavior, return values, and constraints. It could improve by mentioning authentication requirements or error cases, but it provides sufficient context for a read-only tool with simple parameters, compensating for the lack of structured fields.

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 input schema already documents both parameters (userId and username) and their constraints (either/or requirement). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or edge cases, meeting the baseline for 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 specific action ('Get active Instagram stories from a user') and resource ('user by user ID or username'), distinguishing it from siblings like instagram_get_user_posts or instagram_get_user_profile. It explicitly mentions the return content ('story items with media URLs, timestamps, story type, duration, and viewer count'), 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 provides clear context for when to use this tool (to retrieve active stories from a user) and implicitly distinguishes it from siblings by focusing on stories rather than posts or profiles. However, it does not explicitly state when not to use it or name alternatives, such as using instagram_get_user_posts for non-story content.

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 states the action ('Like') and an authentication requirement, but it lacks details on potential side effects (e.g., whether liking is reversible, rate limits, error conditions, or what happens on success). For a mutation tool with zero annotation coverage, this is insufficient.

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 concise—two short sentences that directly state the purpose and a key requirement. Every word earns its place, with no redundant or unnecessary information, making it front-loaded and 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?

Given the tool's complexity (a mutation action with authentication needs) and the lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns, error handling, or behavioral nuances, leaving significant gaps for an AI agent to understand how to use it effectively.

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%, with the parameter 'commentId' fully documented in the schema as 'Comment ID to like'. The description does not add any additional meaning or context beyond what the schema provides, such as format examples or constraints. With high schema coverage, 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 specific action ('Like an Instagram comment') and identifies the target resource ('by comment ID'), which distinguishes it from sibling tools like 'instagram_like_post' that target posts instead of comments. The verb+resource combination is precise and 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 mentions 'Requires authentication' as a prerequisite, which provides some context, but it does not explicitly guide when to use this tool versus alternatives. For example, it does not clarify when to like a comment versus liking a post or commenting on a post, nor does it specify any exclusions or typical use cases.

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 mentions authentication requirements, which is helpful, but fails to describe other critical behaviors such as rate limits, error handling, or whether the action is reversible. For a mutation tool, this leaves significant gaps in understanding its 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 extremely concise—just one sentence that directly states the action and key requirement. It's front-loaded with the core purpose and wastes no words, 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?

Given this is a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., side effects, error cases) and doesn't explain return values or success indicators, leaving the agent with insufficient context for reliable use.

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

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

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as examples or context for parameter usage. 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 action ('Like') and target ('Instagram post or reel by media ID'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'instagram_like_comment', which could cause confusion about when to use each.

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 mentions 'Requires authentication', which provides some context about prerequisites, but offers no guidance on when to use this tool versus alternatives like 'instagram_like_comment' or other engagement tools. There's no explicit when/when-not usage advice.

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 does well by disclosing key behavioral traits: it uses environment variables for credentials, saves the session, and persists across server restarts. This covers authentication method, state management, and persistence, though it could add more on error handling or rate limits.

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 that are front-loaded with the core action and efficiently cover essential details (credentials source and session persistence) without any wasted words. Every sentence adds value, making it highly concise 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 tool's complexity (authentication with session management), no annotations, no output schema, and 0 parameters, the description is fairly complete: it explains what the tool does, how it works, and behavioral aspects. However, it could improve by mentioning prerequisites (e.g., environment variables must be set) or potential outputs, but for a no-param tool, it's sufficient.

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 no parameters need documentation. The description appropriately doesn't discuss parameters, and since there are none, it meets the baseline of 4 for not adding unnecessary information.

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 ('Login to an Instagram account') and the resource ('Instagram account'), making the purpose unambiguous. However, it doesn't differentiate from sibling tools like 'instagram_complete_2fa' which might also be part of authentication flows, so it doesn't fully distinguish from 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 usage by specifying that credentials come from environment variables, suggesting this tool is for initial authentication. However, it doesn't explicitly state when to use this versus alternatives like 'instagram_complete_2fa' for 2FA or provide clear exclusions, leaving some ambiguity in the authentication workflow.

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 behaviors: it invalidates the current session and clears saved data, requiring re-authentication. This covers the mutation aspect and consequences, though it could add more on error handling or 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 front-loaded with the core action and follows with consequences, using two concise sentences with no wasted words. Every sentence adds value, making it efficient and well-structured for quick understanding.

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 complexity (a mutation with no parameters) and lack of annotations or output schema, the description is mostly complete. It explains what the tool does and its effects, but could improve by mentioning potential errors or confirming successful logout. It adequately covers the essentials for this simple operation.

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 no parameter documentation is needed. The description appropriately does not discuss parameters, focusing on the tool's action. A baseline of 4 is applied as it handles the zero-parameter case well without unnecessary details.

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 ('Logout from Instagram') and the resource affected ('clear the saved session'), distinguishing it from sibling tools like instagram_login or other Instagram operations. It precisely defines what the tool does without being tautological.

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 stating 'This will invalidate the current session and require re-authentication for future operations,' which suggests when to use it (e.g., to end a session or reset authentication). However, it does not explicitly mention when not to use it or name alternatives, such as instagram_login for re-establishing a session.

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 mentions the return type ('list of matching accounts with their profile information'), which is helpful, but lacks details on rate limits, authentication requirements, error handling, or pagination. For a search tool with no annotation coverage, this is insufficient to inform safe and effective use.

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 with zero waste: the first states the purpose, and the second specifies the return value. It's front-loaded and efficiently communicates core information without redundancy or fluff.

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

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

Given no annotations and no output schema, the description is incomplete for a search tool in this context. It doesn't address authentication needs (implied by login/logout siblings), rate limits, or what 'profile information' entails (e.g., fields returned). For a tool with 2 parameters and behavioral uncertainty, more context is needed to ensure proper use.

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

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

Schema description coverage is 100%, so the input schema fully documents the parameters ('query' and 'limit'). The description adds no additional semantic meaning beyond what's in the schema (e.g., it doesn't clarify search algorithm, ranking, or what 'profile information' includes). Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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: 'Search for Instagram accounts by username query' specifies the verb (search) and resource (Instagram accounts). It distinguishes from siblings like 'instagram_get_user_profile' by focusing on search rather than retrieval of a specific profile. However, it doesn't explicitly differentiate from all siblings (e.g., 'instagram_get_timeline_feed' also involves retrieval but of different 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 no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'instagram_get_user_profile' for known usernames, or how it relates to other search-like siblings (e.g., 'instagram_get_user_posts' for content search). There's no context on prerequisites, such as whether authentication is required, which is a gap given the sibling tools include login/logout operations.

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