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. While 'Close/delete' implies a destructive mutation, it doesn't specify whether this is reversible, what permissions are required, if there are rate limits, or what happens to associated data. The dual terms 'Close/delete' create ambiguity about the actual effect without further clarification.

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 just three words, making it easy to parse. However, the brevity comes at the cost of clarity—'Close/delete' is ambiguous and could be more precisely phrased. It's front-loaded but under-specified rather than efficiently informative.

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 destructive tool with no annotations and no output schema, the description is inadequate. It doesn't explain the outcome (e.g., success response, error handling), differentiate between 'close' and 'delete', or address safety concerns. Given the complexity of deletion operations, more context is needed to guide 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?

The input schema has 100% description coverage, with the single parameter 'conversation_id' clearly documented. The description adds no additional parameter information beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage without adding value.

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 ('Close/delete') and the resource ('a conversation'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'update_conversation' which might also handle conversation state changes, or explain what 'close' versus 'delete' means in this context.

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. With siblings like 'update_conversation' (which might handle soft deletion or archiving) and 'get_conversation' (for viewing), there's no indication of when deletion is appropriate versus other operations, nor any prerequisites or constraints 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the data source ('anonymous activity pings'), which adds some context, but does not cover critical aspects such as whether this is a read-only operation, potential rate limits, authentication needs, or what the output looks like (e.g., numeric count or structured data). For a tool with no annotations, this leaves significant behavioral gaps.

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, efficient sentence that front-loads the core purpose ('Get active user count') and includes all necessary details (metrics, app specificity, data source) without any redundant or extraneous information. Every word earns its place, 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 moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and data source but lacks details on behavioral traits, output format, and usage guidelines. Without annotations or output schema, the description should do more to compensate, but it provides a minimal viable explanation, leaving room for improvement 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 ('app_id' and 'period') with descriptions. The description adds marginal value by implying the parameters relate to querying user counts for an app over time periods, but it does not provide additional syntax, format details, or constraints beyond what the schema states. 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 specific action ('Get active user count') and resource ('for a specific app'), distinguishing it from sibling tools like 'get_active_users_all_apps' by specifying 'for a specific app' and from 'get_app' by focusing on user metrics rather than app details. It includes precise metrics (DAU/WAU/MAU) and data source ('anonymous activity pings'), making the purpose highly specific and differentiated.

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 specifying 'for a specific app based on anonymous activity pings,' which suggests it's for analytics purposes. However, it does not explicitly state when to use this tool versus alternatives like 'get_active_users_all_apps' (which might cover multiple apps) or other analytics tools, nor does it mention exclusions or prerequisites. The guidance is clear but lacks explicit alternatives or when-not-to-use details.

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 tool queries stats, implying a read-only operation, but does not mention permissions, rate limits, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding its behavior, scoring a 2.

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

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

The description is concise and front-loaded, with two sentences that directly state the tool's purpose and scope. There is no wasted text, and it efficiently communicates key information. However, it could be slightly more structured by explicitly separating purpose from behavioral details, but it's still highly efficient, scoring a 4.

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 (aggregating data across apps), lack of annotations, and no output schema, the description is incomplete. It does not explain the return format, error conditions, or dependencies. For a tool that queries multiple apps' stats, more context is needed to understand its full behavior, resulting in a score of 2.

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 'period' fully documented in the input schema. The description adds no additional parameter semantics beyond what the schema provides, such as format details or examples. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.

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: 'Get active user counts across all apps at once' specifies the verb ('get'), resource ('active user counts'), and scope ('across all apps'). It distinguishes from the sibling 'get_active_users' by emphasizing the 'all apps at once' scope, though it doesn't explicitly name the sibling. This is clear but lacks explicit sibling differentiation, warranting a 4.

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 mentions 'Queries each app's DAU/WAU/MAU stats,' which implies usage for aggregated metrics, but does not specify contexts, exclusions, or compare to other tools like 'get_active_users' or 'get_app'. Without explicit when/when-not instructions, it scores a 2.

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 tool retrieves details but doesn't cover critical aspects like whether it's a read-only operation, authentication requirements, error handling (e.g., for invalid app IDs), rate limits, or response format. 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 a single, efficient sentence with zero waste. It front-loads the core purpose ('Get details for a specific app') and adds specific inclusions ('configuration and notification settings') without unnecessary elaboration. 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?

Given the tool's simplicity (1 parameter, 100% schema coverage) but lack of annotations and output schema, the description is incomplete. It doesn't address behavioral traits like safety, response format, or error conditions, which are crucial for an agent to use it correctly. The description alone is insufficient for reliable invocation.

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

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

Schema description coverage is 100%, with the single parameter 'app_id' fully documented in the schema as 'The app ID (app_...)'. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. Baseline 3 is appropriate since the schema handles 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 tool's purpose with a specific verb ('Get') and resource ('app details'), specifying what details are included ('configuration and notification settings'). It distinguishes from sibling 'list_apps' by focusing on a single app rather than listing multiple. However, it doesn't explicitly contrast with other get_* tools like 'get_conversation' or 'get_customer' beyond the 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an app ID), exclusions, or comparisons to siblings like 'list_apps' for browsing or other get_* tools for different resources. Usage is implied by the name and description but not explicitly stated.

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 but only states what the tool does, not how it behaves. It doesn't disclose whether this is a read-only operation, what permissions are needed, potential rate limits, error conditions, or what format the returned conversation data takes. The description adds no behavioral context beyond the basic function.

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, efficient sentence that states the core function without unnecessary words. It's appropriately sized for a simple retrieval tool and front-loads the essential 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?

For a simple retrieval tool with one fully documented parameter, the description is minimally adequate. However, with no annotations and no output schema, it should ideally provide more context about return format, error handling, or permissions. The description covers the basic purpose but leaves behavioral aspects unspecified.

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

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

Schema description coverage is 100%, so the schema fully documents the single required parameter. The description adds no additional parameter information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.

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

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

The description clearly states the action ('Get') and resource ('a single conversation with its messages and tags'), making the purpose understandable. It distinguishes from list_conversations by specifying retrieval of a single conversation with full details, but doesn't explicitly contrast with update_conversation or delete_conversation.

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 about when to use this tool versus alternatives. The description doesn't mention prerequisites (like needing a conversation ID), when not to use it, or how it differs from other conversation-related tools like list_conversations or update_conversation.

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 this is a 'Get' operation, implying read-only behavior, but doesn't confirm if it's safe, whether it requires authentication, or if there are rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

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, efficient sentence that front-loads the core purpose and lists specific data fields. There's no wasted language, and it directly communicates what the tool does without 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 simplicity (0 parameters, no output schema), the description is adequate for a basic read operation. However, without annotations or output schema, it lacks details on return format, error handling, or authentication needs. It meets minimum viability but doesn't fully compensate for the missing structured data.

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, and schema description coverage is 100%, so there's no need for parameter documentation in the description. The baseline for this scenario is 4, as the description appropriately avoids redundant information about inputs that don't 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 verb 'Get' and the resource 'current team/customer info', specifying what information is retrieved (tier, mascot settings, brand color). It distinguishes from siblings like 'get_active_users' or 'list_team' by focusing on organizational metadata rather than user lists or conversations. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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 like 'list_team' or 'get_dashboard'. It doesn't mention prerequisites, exclusions, or specific contexts where this tool is preferred. The agent must infer usage from the purpose alone, which is insufficient for clear decision-making.

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 describes the content of the dashboard but lacks critical behavioral details: it doesn't specify if this is a read-only operation, whether it requires authentication, any rate limits, data freshness, or what the output format looks like. For a tool with no annotations, this is a significant gap in 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, efficient sentence that lists key dashboard components without unnecessary words. It's front-loaded with the main action ('Get analytics dashboard') and follows with specifics. However, it could be slightly more structured by grouping related metrics or indicating priority, but it remains highly 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 the complexity of an analytics tool with no annotations and no output schema, the description is incomplete. It details what the dashboard includes but omits behavioral aspects (e.g., read-only status, permissions), output format, and usage context. For a tool that likely returns rich data, this leaves significant gaps for an AI agent to infer 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 description mentions no parameters, while the input schema has one parameter ('period') with 100% schema description coverage. Since the schema fully documents the parameter, the description doesn't need to add param info, but it also doesn't compensate or provide additional context. This meets the baseline of 3 for high schema coverage without description enhancement.

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: 'Get analytics dashboard' with specific content details like 'conversation stats, response times, breakdowns by category/status/priority/app, daily trends, and team activity.' It uses a specific verb ('Get') and resource ('analytics dashboard'), though it doesn't explicitly differentiate from sibling tools like 'get_sentiment_insights' or 'get_active_users' which might also provide analytics-related data.

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 lists what the dashboard includes but doesn't mention when to choose it over other analytics tools like 'get_sentiment_insights' or 'get_active_users', nor does it specify prerequisites, exclusions, or optimal use cases. This leaves the agent with minimal contextual direction.

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 full burden. It mentions ranking by vote count and de-duplication, which are useful behavioral traits. However, it doesn't disclose important aspects like pagination behavior, rate limits, authentication requirements, or what happens when no filters are applied. For a read operation with no annotation coverage, this leaves significant gaps.

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, efficient sentence that communicates the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every element 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?

For a read operation with 4 well-documented parameters but no output schema and no annotations, the description provides adequate basic context about what's being retrieved. However, it doesn't explain the return format, result structure, or error conditions. Given the complexity and lack of output schema, this leaves the agent with incomplete information about what to expect.

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

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

Schema description coverage is 100%, so the schema fully documents all 4 parameters. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

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 'feature requests' with specific characteristics: 'AI-extracted', 'ranked by vote count', and 'auto-extracted from user feedback and de-duplicated'. It distinguishes from generic list operations but doesn't explicitly differentiate from sibling tools like 'merge_feature_requests' or 'update_feature_request'.

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 like 'merge_feature_requests' or 'update_feature_request'. It doesn't mention prerequisites, appropriate contexts, or exclusions. The agent must infer usage from the tool name and description alone.

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 tool is 'Powered by AI classification,' hinting at automated analysis, but doesn't cover critical aspects like whether it's read-only, requires permissions, has rate limits, or what the output format entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 highly concise and front-loaded, consisting of a single sentence that directly states the tool's function and an additional clarifying phrase. Every word earns its place, with no redundant information, 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 the lack of annotations and output schema, the description is incomplete for a tool that performs sentiment analysis. It doesn't explain the return values (e.g., breakdown format), error conditions, or behavioral constraints like data freshness or access requirements. For a tool with no structured output information, this leaves the agent under-informed.

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 two parameters ('period' and 'app_id'). The description adds marginal value by implying analysis across 'app, category, and time period,' which aligns with parameters but doesn't provide additional syntax or format details beyond the schema. 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: 'Get sentiment analysis breakdown (positive/neutral/negative) by app, category, and time period.' It specifies the verb ('Get'), resource ('sentiment analysis breakdown'), and dimensions of analysis. However, it doesn't explicitly differentiate from sibling tools like 'get_conversation' or 'list_conversations' that might also involve sentiment-related data, keeping it from a perfect score.

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 mentions 'by app, category, and time period' but doesn't specify prerequisites, exclusions, or compare to siblings like 'get_conversation' for detailed sentiment. Without explicit usage context, the agent lacks direction on tool selection.

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 but only states it lists apps with usage stats. It doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or what 'usage stats' entails (e.g., format, recency). This leaves significant gaps for a tool that likely returns data.

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, efficient sentence that front-loads the core action ('List all registered apps') and adds useful detail ('with usage stats'). There is no wasted text, 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, no output schema, and a read operation with potential complexity (usage stats), the description is incomplete. It doesn't explain return values, error conditions, or behavioral constraints. For a tool that likely returns structured data, this leaves the agent under-informed.

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, and schema description coverage is 100% (empty schema). The description doesn't need to add parameter semantics, so it meets the baseline of 4 for zero-parameter tools. No additional value is required beyond stating the purpose.

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 ('List') and resource ('all registered apps for the current team'), with additional context about 'usage stats'. It distinguishes this from siblings like 'get_app' (singular) and 'get_active_users_all_apps' (users, not apps). However, it doesn't explicitly differentiate from all possible list operations, keeping it at 4 rather than 5.

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 prerequisites (e.g., team context), exclusions, or compare it to siblings like 'get_app' for single app details. Usage is implied by the purpose but not explicitly stated.

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 that the tool 'Returns conversations with status, priority, unread counts, and pagination,' which adds some context about output and pagination behavior. However, it fails to disclose critical traits such as whether this is a read-only operation, potential rate limits, authentication requirements, or any side effects, leaving significant gaps for a tool with 9 parameters.

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

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

The description is concise and front-loaded, stating the core purpose in the first clause. It consists of two sentences that efficiently cover listing, filtering, and return details without unnecessary elaboration. However, the second sentence could be slightly more structured to separate return values from behavioral aspects, but overall it's well-sized and avoids 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 complexity (9 parameters, no output schema, and no annotations), the description is moderately complete. It covers the basic action and return fields but lacks details on behavioral traits, error handling, and sibling tool differentiation. Without annotations or an output schema, more context on permissions, rate limits, and response structure would improve completeness for a listing tool with multiple filters.

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, providing detailed documentation for all 9 parameters, including enums and defaults. The description adds minimal value beyond the schema by mentioning 'optional filters' and hinting at the return fields, but it doesn't elaborate on parameter interactions or usage nuances. With high schema coverage, a baseline score of 3 is appropriate as the description doesn't significantly enhance 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 clearly states the tool's purpose: 'List conversations with optional filters.' It specifies the verb ('List') and resource ('conversations'), and mentions filtering capabilities. However, it doesn't explicitly differentiate from sibling tools like 'get_conversation' (which likely retrieves a single conversation) or 'update_conversation' (which modifies conversations), missing full sibling distinction.

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 sibling tools like 'get_conversation' for single conversations or 'get_active_users' for user data, nor does it specify prerequisites or exclusions. Usage is implied through the action of listing with filters, but explicit context is lacking.

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 tool lists team members, implying a read-only operation, but doesn't cover aspects like pagination, rate limits, authentication needs, or error handling. This is a significant gap for a tool with zero 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 a single, efficient sentence: 'List all team members with their roles.' It's front-loaded with the core action and resource, with zero wasted words, 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 simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, or usage context. For a basic list tool, this is borderline complete but leaves gaps that could hinder effective 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 0 parameters with 100% coverage, meaning no parameters are documented in the schema. The description doesn't add parameter details, but since there are no parameters, this is acceptable. A baseline of 4 is appropriate as the description doesn't need to compensate for missing param info.

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: 'List all team members with their roles.' It uses a specific verb ('List') and identifies the resource ('team members with their roles'). However, it doesn't explicitly distinguish itself from sibling tools like 'get_active_users' or 'get_active_users_all_apps,' which might have overlapping functionality, preventing a perfect score.

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 prerequisites, exclusions, or compare it to siblings such as 'get_active_users' or 'get_active_users_all_apps,' leaving the agent to infer usage based on name alone.

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: merging vote counts and linked conversations, and deleting the source. However, it does not cover aspects like permissions needed, error handling, or whether the operation is atomic. No contradictions exist, and it adds valuable context beyond what the schema provides.

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, efficient sentence that front-loads the purpose and key actions. Every word earns its place, with no redundancy or unnecessary details, making it highly concise 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 destructive merge operation) and lack of annotations or output schema, the description is reasonably complete. It covers the main action and consequences, but could improve by mentioning potential side effects or return values. For a tool with no structured behavioral hints, it provides adequate context, though not exhaustive.

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 clear descriptions for both parameters ('target_id' and 'source_id'). The description adds minimal semantic value beyond the schema, as it does not explain parameter interactions or constraints (e.g., that IDs must be valid or distinct). The baseline score of 3 is appropriate since the schema already documents the parameters well.

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 ('merge a duplicate feature request into a target'), the resource ('feature request'), and the outcome ('combines vote counts and linked conversations, then deletes the source'). It distinguishes itself from sibling tools like 'delete_conversation' or 'update_feature_request' by focusing on merging duplicates rather than simple deletion or updates.

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 merging duplicate feature requests, but does not explicitly state when to use this tool versus alternatives like 'update_feature_request' or 'delete_conversation'. It provides context (merging duplicates) but lacks explicit exclusions or prerequisites, such as whether both IDs must exist or if the operation is reversible.

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 the reply types but fails to cover critical aspects like required permissions, rate limits, whether the action is reversible, or what happens on success/failure. This is inadequate for a mutation tool with zero 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 a single, efficient sentence that front-loads the core purpose and adds a useful detail about reply types. There is no wasted verbiage, making it appropriately sized and well-structured.

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

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

Given the complexity of a mutation tool with no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits, error handling, and return values, which are essential for an agent to use this tool effectively 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 input schema already documents all parameters thoroughly. The description adds no additional meaning beyond what the schema provides, such as explaining parameter interactions or constraints, 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 verb ('Send') and resource ('a reply message in a conversation'), specifying it can be either visible or internal. However, it doesn't explicitly distinguish this from sibling tools like 'update_conversation', which might also involve sending messages, leaving some 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 provides no guidance on when to use this tool versus alternatives such as 'update_conversation' or other messaging-related operations. It mentions the types of replies but offers no context on prerequisites, timing, or 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It states this is an update operation (implying mutation), but doesn't disclose important traits like required permissions, whether changes are reversible, rate limits, or what happens to unspecified fields (e.g., are they preserved?). For a mutation tool with zero annotation coverage, this leaves significant gaps.

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, efficient sentence that front-loads the core action ('Update conversation') followed by the modifiable fields. There's no wasted text, though it could be slightly more structured (e.g., separating required vs. optional fields).

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 doesn't cover behavioral aspects (permissions, side effects), response format, or error handling. While the schema covers parameters well, the overall context for safe and effective use is lacking.

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 6 parameters thoroughly with descriptions and enum values. The description adds minimal value beyond what's in the schema—it lists the same fields but without the detailed constraints. 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 verb ('Update') and resource ('conversation'), and specifies the fields that can be modified (status, priority, assignment, title, category). It distinguishes from sibling tools like 'get_conversation' (read-only) and 'delete_conversation' (destructive), but doesn't explicitly differentiate from 'update_feature_request' which updates a 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing conversation_id), when not to use it (e.g., for creating new conversations), or how it relates to sibling tools like 'send_reply' (which might also update conversation status). Usage is implied but not explicitly stated.

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 states it's an update operation, implying mutation, but doesn't disclose critical traits: whether it requires specific permissions, if changes are reversible, what happens to other fields not mentioned, error handling, or response format. This is inadequate for a mutation tool with zero 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 a single, efficient sentence with zero waste. It front-loads the core action and scope ('Update the status or title of a feature request'), making it immediately understandable. Every word earns its place, and 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 complexity (a mutation operation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or return values, which are crucial for safe and effective use. The schema handles parameters well, but overall context is insufficient for a mutation tool.

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

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

The schema description coverage is 100%, with clear descriptions for all parameters (e.g., request_id format, status enum values, title purpose). The description adds minimal value beyond the schema by mentioning 'status or title', but doesn't provide additional semantics like formatting rules or constraints. Baseline 3 is appropriate as the schema does the heavy lifting.

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

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

The description clearly states the action ('update') and the resource ('feature request'), specifying what can be updated ('status or title'). It distinguishes from siblings like 'merge_feature_requests' or 'get_feature_requests' by focusing on modification rather than retrieval or merging. However, it doesn't explicitly differentiate from 'update_conversation', which is a similar update operation on a different resource.

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 prerequisites (e.g., needing a valid request_id), exclusions (e.g., cannot update other fields beyond status/title), or comparisons to siblings like 'merge_feature_requests' for combining requests or 'get_feature_requests' for viewing. Usage is implied by the action but lacks explicit context.

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