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

No annotations are provided, so the description carries full burden. While it mentions what the tool analyzes, it doesn't disclose behavioral traits like whether this is a read-only analysis, what permissions are needed, whether it modifies code, what the output format looks like, or any rate limits. For a code analysis tool with no annotations, 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 states the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information about what the tool does.

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 code analysis tool with 4 parameters and no annotations or output schema, the description is insufficient. It doesn't explain what the analysis produces, how results are returned, whether this is a read-only operation, or what happens when files are provided versus not provided. The description should provide more context about the tool's behavior and output.

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 mentions the analysis focus areas (architecture, performance, security, quality) which aligns with the 'focus' parameter enum, but adds no additional semantic context beyond what's in the schema. Baseline 3 is appropriate when 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 tool's purpose: analyzing code for specific issue types (architecture, performance, security, quality). It uses a specific verb ('analyze') and resource ('code'), but doesn't explicitly distinguish from siblings like 'review-code' or 'ultra-analyze', which appear to have overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'review-code', 'debug-issue', and 'ultra-analyze' available, there's no indication of when this specific analysis tool is preferred or what differentiates it from similar tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it states the tool 'challenges with critical thinking,' it doesn't describe what this actually means operationally - what form the challenge takes, whether it's interactive or one-way, what permissions or constraints apply, or what the output looks like. This leaves significant behavioral uncertainty.

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 at just 6 words, front-loading the core purpose without any wasted words. Every word earns its place in communicating the essential function. This is model efficiency in technical documentation.

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 tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'challenging with critical thinking' means in practice, what the output format is, or how this differs from similar tools. The agent would have significant uncertainty about how to properly use this tool and interpret its results.

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

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

The description adds no parameter information beyond what's already in the schema, which has 100% coverage. The schema fully documents the single 'prompt' parameter with clear usage instructions. Since schema coverage is high, the baseline score of 3 is appropriate - the description doesn't add value but the schema already provides complete 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 tool's purpose: 'Challenge a statement or assumption with critical thinking.' This specifies the verb ('challenge') and the target ('statement or assumption'), but doesn't distinguish it from sibling tools like 'ultra-challenge' or explain how it differs from other analysis tools like 'analyze-code' or 'investigate'.

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. There are multiple sibling tools for analysis (e.g., 'analyze-code', 'investigate', 'ultra-challenge'), but the description doesn't indicate when this specific critical thinking challenge tool is appropriate versus those other options.

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 the action without behavioral details. It doesn't disclose that this is a destructive operation (permanently removes vectors), potential side effects (e.g., affecting search performance), or any permissions/rate limits needed, which is a significant gap for a mutation tool.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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 destructive nature (clearing data), lack of annotations, and no output schema, the description is incomplete. It should explain what 'clear' entails (e.g., irreversible deletion), confirmations needed, or expected outcomes, but provides only minimal 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?

The description adds no parameter semantics beyond what the schema provides, as schema coverage is 100% with a clear description for the 'path' parameter. The baseline score of 3 reflects adequate coverage by the schema alone, with no extra value from the 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 action ('clear') and target ('all indexed vectors for a project'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search-vectors' or 'index-vectors', which would require mentioning it's a destructive operation versus those read-only or creation 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing indexed vectors first), exclusions (e.g., not for partial clearing), or refer to sibling tools like 'index-vectors' for re-indexing after clearing, leaving usage context implied at best.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Get consensus' implies a read-only operation, it doesn't specify whether this makes API calls to external services, what the output format looks like, whether there are rate limits, or what happens with the 'files' parameter. For a tool that likely interacts with multiple AI models, this is insufficient behavioral context.

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 purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information. Every word earns its place in this concise formulation.

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 tool with 3 parameters (including a complex array of model objects), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'consensus' means in practice, what the output looks like, or how the tool behaves operationally. The agent would need to guess about the tool's behavior and results.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain how 'models' interact, what 'consensus' means operationally, or how 'files' are used. 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 tool's purpose as 'Get consensus from multiple AI models on a proposal' - a specific verb ('Get consensus') with resource ('multiple AI models') and target ('on a proposal'). However, it doesn't differentiate from sibling tools like 'analyze-code' or 'research' which might also involve AI analysis, 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 provides no guidance on when to use this tool versus alternatives. There's no mention of when this tool is appropriate versus tools like 'analyze-code', 'research', or 'ultra-analyze', nor any context about prerequisites or limitations. The agent must infer usage from the 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 full burden for behavioral disclosure. It mentions a 'systematic problem-solving approach' but doesn't explain what this entails operationally—such as whether it performs analysis, generates solutions, requires specific permissions, has rate limits, or what the output format might be. This leaves significant gaps for a tool with 4 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 a single, efficient sentence that front-loads the core purpose. However, it could be more structured by explicitly mentioning key parameters or differentiating from siblings, but it avoids unnecessary verbosity.

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 implied by 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't clarify what the tool actually does (e.g., analysis, solution generation), how it interacts with the AI provider parameter, or what results to expect, leaving too much ambiguity for effective 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 fully documents all 4 parameters (task, files, symptoms, provider). The description adds no additional meaning beyond what's in the schema, such as explaining relationships between parameters or usage examples. 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 'Debug technical issues with systematic problem-solving approach' states a general purpose but lacks specificity about what resources it operates on or how it differs from similar tools like 'investigate', 'ultra-debug', or 'tracer'. It mentions 'technical issues' but doesn't specify whether this is for code, systems, or other domains, making it somewhat vague.

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

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

No guidance is provided on when to use this tool versus alternatives like 'investigate', 'ultra-debug', or 'tracer' from the sibling list. The description implies a debugging context but offers no explicit when/when-not criteria or prerequisites for usage.

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

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

No annotations are provided, so the description carries full burden for behavioral disclosure. While it mentions support for specific models and Google Search integration, it doesn't describe important behavioral aspects like rate limits, authentication requirements, cost implications, response formats, or error handling. For a complex AI tool with 8 parameters, this leaves significant gaps in understanding how the tool behaves in practice.

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 concise with two sentences that efficiently convey the core functionality and model support. It's front-loaded with the main purpose and follows with specific implementation details. There's no wasted language, though it could potentially benefit from slightly more structure to separate purpose from technical details.

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 8-parameter AI tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, error conditions, or practical constraints. While the schema covers parameter details, the description fails to provide the broader context needed to effectively use this tool for 'deep reasoning' tasks, especially compared to the many alternative tools available.

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 8 parameters thoroughly. The description adds minimal parameter semantics beyond what's in the schema - it mentions GPT-5 and Gemini 2.5 Pro specifically (which relate to the 'model' parameter) and Google Search for Gemini (related to 'enableSearch'), but these are already implied in the schema descriptions. With high schema coverage, the baseline is 3 even without significant param info in the 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 states the tool uses advanced AI models for deep reasoning and complex problem-solving, which provides a general purpose. However, it's somewhat vague about what constitutes 'deep reasoning' versus other AI tasks, and it doesn't clearly distinguish this tool from sibling tools like 'analyze-code', 'research', or 'investigate' which might also involve AI reasoning. The mention of specific model support adds some specificity but doesn't fully clarify the unique role.

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 many sibling tools that might involve AI or reasoning (like 'research', 'analyze-code', 'investigate'), there's no indication of what types of problems are best suited for 'deep-reasoning' versus those other tools. The description mentions model support but doesn't explain when to choose this tool over other AI-related tools in the server.

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 offers minimal behavioral insight. It mentions 'various formats' but doesn't disclose whether this is a read-only operation, if it modifies files, what permissions are needed, rate limits, or what the output looks like. For a tool with 4 parameters and no annotations, this is inadequate disclosure.

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

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

Extremely concise single sentence with zero wasted words. The description is front-loaded and efficiently communicates the core function 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?

For a 4-parameter tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool actually produces, how it interacts with files, whether it's a generation or analysis tool, or how it differs from similar documentation-related siblings. The agent lacks critical context for proper tool selection and 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%, so the schema fully documents all 4 parameters. The description adds no additional parameter semantics beyond implying format variety through 'various formats'. This meets the baseline of 3 when schema coverage is complete.

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

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

The description 'Generate documentation in various formats' states a general purpose but lacks specificity about what resources are documented or how it differs from siblings like 'ultra-docs'. It mentions 'various formats' which provides some scope, but doesn't specify the verb's target beyond 'documentation'.

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 on when to use this tool versus alternatives like 'ultra-docs' or 'review-code'. The description provides no context about appropriate use cases, prerequisites, or exclusions. The agent must infer usage solely from the tool name and parameters.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'indexing' and 'semantic search,' implying a write operation that creates or updates indices, but doesn't disclose critical traits like whether indexing is incremental or full, potential performance impacts, rate limits, or authentication needs. For a mutation tool with zero annotation coverage, this is a significant gap, warranting a score of 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 a single, efficient sentence: 'Index project files for semantic search using vector embeddings.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. This earns a perfect score for conciseness and structure.

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

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

Given the tool's complexity (a mutation operation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'indexing' entails (e.g., file processing, embedding generation), potential side effects, or return values. For a tool that modifies state without structured safety hints, more context is needed, 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 input schema has 100% description coverage, with clear documentation for all three parameters (path, provider, force). The description adds no additional parameter semantics beyond what's in the schema, such as explaining the implications of 'force' re-indexing or provider selection. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Index project files for semantic search using vector embeddings.' It specifies the verb ('index'), resource ('project files'), and method ('using vector embeddings'), which is specific and informative. However, it doesn't explicitly distinguish this tool from sibling tools like 'search-vectors' or 'clear-vectors', which limits the score to 4 instead of 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 sibling tools like 'search-vectors' (for searching indexed files) or 'clear-vectors' (for removing indices), nor does it specify prerequisites, such as needing configured embedding providers. Without any usage context or exclusions, the score is 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'configurable depth' but doesn't explain what 'thoroughly' entails—whether this tool performs web searches, uses AI models, returns structured outputs, or has rate limits. For a tool with 5 parameters and 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 front-loads the core purpose ('Investigate topics thoroughly') and adds a key feature ('with configurable depth'). There is no wasted verbiage or redundancy.

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

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

Given the tool's complexity (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what the tool actually does (e.g., AI analysis, web search), what outputs to expect, or how it differs from similar siblings. For a tool named 'investigate' in a context with many analytical siblings, this leaves too much ambiguity.

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 no additional meaning beyond implying that 'depth' affects thoroughness, but this is already clear from the schema's enum values. 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 'Investigate topics thoroughly with configurable depth' states the verb (investigate) and resource (topics) but is vague about what 'investigate' means in this context. It doesn't specify whether this involves web search, AI analysis, or other methods, nor does it distinguish from siblings like 'research' or 'deep-reasoning' which might have overlapping purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'research' or 'deep-reasoning' from the sibling list. It mentions configurable depth but doesn't explain what scenarios warrant shallow vs. deep investigations or when other tools might be more appropriate.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'List' implies a read-only operation, the description doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the output takes. For a tool with zero annotation coverage, this is inadequate.

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 directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, with every word contributing to clarity.

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 complete. However, it lacks behavioral details like output format or authentication requirements, which would be helpful for an agent. The absence of an output schema means the description should ideally hint at return values, but it doesn't.

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 description appropriately doesn't mention parameters, which is correct for a parameterless tool, earning a baseline score of 4.

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 available AI models and their configuration status'), making the purpose unambiguous. However, it doesn't explicitly differentiate this tool from potential sibling tools that might also list models, though none appear in the provided sibling list.

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, context for usage, or any exclusions. With no annotations and many sibling tools, this lack of guidance is a significant gap.

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 'step-by-step approach' but doesn't explain what that entails—e.g., whether it generates a detailed plan, requires specific inputs beyond the schema, or has limitations like rate constraints. For a tool with no annotations, 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 a single, efficient sentence: 'Plan feature implementation with step-by-step approach.' It's front-loaded with the core purpose and wastes no words, 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 complexity of a planning tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, output format, and usage context, leaving the agent with insufficient information to effectively invoke the tool beyond basic parameter input.

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 adds no parameter-specific information beyond what the schema provides. Since schema description coverage is 100%, the baseline score is 3. The description doesn't clarify how parameters like 'scope' or 'provider' affect the planning process, so it doesn't compensate for potential gaps in schema semantics.

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

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

The description clearly states the tool's purpose: 'Plan feature implementation with step-by-step approach.' It specifies the verb ('Plan') and resource ('feature implementation'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'planner' or 'ultra-plan,' which appear related.

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 'planner' and 'ultra-plan' present, there's no indication of context, prerequisites, or exclusions. Usage is implied by the name 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 for behavioral disclosure. 'Multi-step planning with revisions and branches' suggests iterative, branching planning capabilities but doesn't describe what the tool actually produces (e.g., plans, outlines, structured outputs), how revisions work, what branching entails, or any limitations. It mentions features but not their implementation or behavioral characteristics.

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 at just 5 words. It's front-loaded with the core concept and wastes no words. Every word contributes to the basic understanding of the tool's purpose. This is appropriate brevity for a tool name that already suggests its domain.

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 complex planning tool with 11 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how planning sessions are structured, what 'revisions' and 'branches' mean operationally, or how the planning process works. The agent would struggle to understand what invoking this tool actually accomplishes.

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 11 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. It doesn't explain how parameters interact (e.g., how 'isRevision' relates to 'revisingStep') or provide usage examples. Baseline 3 is appropriate when 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 'Multi-step planning with revisions and branches' states the general purpose but is vague about what the tool actually does. It mentions planning features but doesn't specify what kind of planning (e.g., project planning, AI planning, strategic planning) or what resources it operates on. It doesn't clearly distinguish from sibling tools like 'plan-feature' or 'ultra-plan'.

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. There's no mention of prerequisites, appropriate contexts, or when to choose this over similar planning tools like 'plan-feature' or 'ultra-plan'. The agent must infer usage from the tool name and parameters 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. The description mentions 'validation' but doesn't explain what the tool actually does behaviorally—whether it runs automated checks, provides recommendations, blocks commits, or returns analysis results. It lacks details on permissions, side effects, rate limits, or output format, leaving significant gaps for an 8-parameter tool.

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

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

The description is a single, efficient sentence with no wasted words. It's front-loaded and clear in its brevity, though it could benefit from more detail given the tool's complexity. The structure is appropriate for a short description, but it may be too concise for adequate 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 (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what validation entails, how results are returned, or behavioral traits. For a tool with rich parameters but no structured behavioral hints, the description fails to provide sufficient context for effective use, leaving too much undefined.

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 8 parameters. The description adds no additional meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or practical examples. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't 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 'Pre-commit validation for code changes' states the general purpose but is vague. It specifies the action ('validation') and target ('code changes') but lacks specificity about what validation entails or how it differs from sibling tools like 'review-code' or 'secaudit'. The title 'Pre-commit Validation' is essentially restated, making it somewhat 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?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like 'review-code', 'secaudit', and 'analyze-code' available, the description offers no context about when pre-commit validation is appropriate versus other code analysis tools. Usage is implied only by the tool's name, not explained.

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 'comprehensive research' and 'multiple output formats', but does not explain what 'comprehensive' entails (e.g., depth, sources, time), how outputs differ, or any operational traits like rate limits, authentication needs, or potential side effects. This leaves significant gaps for a tool with 5 parameters and no output schema.

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 is front-loaded with the core action ('conduct comprehensive research'). It avoids unnecessary words, though it could be more structured by explicitly listing key capabilities. Every part earns its place, making it concise but slightly under-specified.

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 research tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on what 'research' involves, how results are returned, error handling, or behavioral constraints. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter input.

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

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

The schema description coverage is 100%, so the schema already documents all parameters well. The description adds no specific meaning beyond the schema, such as explaining how 'sources' interact with 'query' or what 'academic' output entails. With high schema coverage, 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 states the tool 'conduct[s] comprehensive research with multiple output formats', which provides a general purpose (research) and mentions output formats. However, it lacks specificity about what 'research' entails (e.g., web search, document analysis, data synthesis) and does not clearly distinguish it from sibling tools like 'investigate' or 'search-vectors', making it somewhat vague.

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 explicit guidance is provided on when to use this tool versus alternatives. The description does not mention any context, prerequisites, or exclusions, and with sibling tools like 'investigate' and 'search-vectors' present, it fails to differentiate usage scenarios, offering minimal direction to the agent.

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 mentions what the tool reviews but doesn't describe how it works (e.g., AI-based analysis, static analysis), what permissions or authentication might be needed, whether it modifies code, rate limits, or output format. For a tool with no annotation coverage, 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 directly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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 code review (which could involve AI providers, file analysis, etc.), no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects, usage context, or what to expect from the tool's operation, leaving significant gaps for an agent 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?

Schema description coverage is 100%, so the schema fully documents all four parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'task' or 'focus' in more detail). 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 tool's purpose: 'Review code for bugs, security issues, performance, or style problems.' It specifies the verb ('review') and resource ('code') with concrete review areas. However, it doesn't distinguish this tool from sibling tools like 'analyze-code' or 'ultra-review,' which likely have overlapping functionality.

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

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

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (e.g., 'analyze-code,' 'ultra-review,' 'secaudit'), there's no indication of when this specific review tool is appropriate, what prerequisites might exist, or when other tools might be better suited.

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 mentions the search functionality but doesn't describe what happens during execution (e.g., does it modify data, require authentication, have rate limits, or return structured results?). For a search tool with no annotation coverage, this leaves significant behavioral aspects undocumented.

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 gets straight to the point with zero wasted words. It's appropriately sized for a search tool and front-loads the core functionality 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 moderate complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose clearly but lacks behavioral context, usage guidelines, and output information. With no annotations to supplement, this leaves gaps in understanding how the tool behaves and when to use it.

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

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

Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'natural language queries' map to the 'query' parameter or provide examples). Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 files and code snippets using natural language queries'. It specifies the verb ('Search'), resource ('files and code snippets'), and method ('natural language queries'). However, it doesn't differentiate from sibling tools like 'analyze-code' or 'investigate' which might also involve searching or examining code.

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 many sibling tools like 'analyze-code', 'investigate', and 'research', there's no indication of this tool's specific context or prerequisites. It simply states what it does without any usage boundaries.

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. 'Security audit' implies a read-only analysis operation, but the description doesn't clarify whether this tool makes changes, requires specific permissions, has rate limits, or produces destructive outputs. It mentions 'audit for code and configurations' but doesn't describe what the audit actually does (e.g., static analysis, vulnerability scanning, compliance checking) or what format the results take.

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 at just 5 words: 'Security audit for code and configurations'. It's front-loaded with the core purpose and wastes no words. Every word earns its place by specifying both the action (audit) and targets (code and configurations).

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 complex tool with 8 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the audit actually produces, how results are formatted, whether it's interactive or batch, or what happens after the audit. The description leaves too many behavioral questions unanswered given the tool's complexity and the lack of structured metadata.

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 8 parameters thoroughly with descriptions and enums. The description adds no parameter-specific information beyond what's in the schema. It mentions 'code and configurations' which loosely relates to the 'files' and 'securityScope' parameters but provides no additional semantic context. 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 'Security audit for code and configurations' states what the tool does at a high level (verb+resource), but it's vague about scope and implementation. It doesn't distinguish this security audit tool from sibling tools like 'analyze-code', 'review-code', or 'ultra-review' that might also analyze code. The purpose is clear enough to understand the general function but lacks specificity about what makes this audit unique.

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 multiple code analysis and review tools in the sibling list (analyze-code, review-code, ultra-review, etc.), there's no indication of when a security audit is preferred over general code analysis or code review. No prerequisites, constraints, or comparison to other tools are mentioned.

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

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

With no annotations provided, the description carries full burden but only states high-level intent without disclosing behavioral traits. It doesn't mention what gets traced (e.g., code execution, dependencies), output format, permissions needed, rate limits, or side effects. 'Debug complex issues' is too vague to inform agent 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 a single, efficient sentence without wasted words. It's front-loaded with the core purpose, though it could be more structured by explicitly separating tracing from debugging aspects.

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 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what tracing entails, what the output looks like, or how it integrates with debugging, leaving significant gaps for a tool with multiple configuration options.

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 parameters are well-documented in the schema itself. The description adds no additional meaning about parameters beyond what's in the schema, maintaining the baseline score of 3 for adequate 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 'Trace execution flow and debug complex issues' states a general purpose but lacks specificity about what resources are traced (code, systems, etc.) and doesn't distinguish from sibling tools like 'debug-issue' or 'analyze-code'. It uses vague terms like 'complex issues' without clarifying scope.

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 on when to use this tool versus alternatives like 'debug-issue', 'analyze-code', or 'investigate'. The description implies debugging but doesn't specify context or exclusions, leaving the agent to guess based on tool names 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 full burden but offers minimal behavioral disclosure. It mentions 'step-by-step workflow' suggesting iterative execution, but doesn't describe what 'comprehensive code analysis' entails operationally—such as whether it modifies code, requires specific permissions, has rate limits, or what the output format looks like. For a complex 10-parameter tool with no annotations, this leaves significant gaps in understanding how the tool 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 phrase that's front-loaded with the core purpose. It wastes no words, though it could be more specific. For a tool with this complexity, it might be too brief, but it's structurally sound and not verbose.

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 high complexity (10 parameters, no annotations, no output schema) and the description's vagueness, it's incomplete. The description doesn't adequately explain what 'comprehensive code analysis' means, how the step-by-step workflow operates, or what results to expect. For a tool with many parameters and no structured behavioral hints, more descriptive context is needed to guide effective 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 all 10 parameters thoroughly. The description adds no specific parameter information beyond what's in the schema—it doesn't explain relationships between parameters like 'stepNumber' and 'totalSteps', or how 'findings' accumulates. Baseline 3 is appropriate as the schema does the heavy lifting, but the description doesn't compensate with additional semantic context.

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 'Comprehensive code analysis with step-by-step workflow' states a general purpose but lacks specificity about what distinguishes it from siblings like 'analyze-code', 'review-code', or 'ultra-review'. It mentions 'step-by-step workflow' which hints at iterative analysis, but doesn't clearly differentiate the tool's unique function or scope compared to similar tools in the server.

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 explicit guidance on when to use this tool versus alternatives like 'analyze-code', 'review-code', or 'ultra-review' is provided. The description implies a comprehensive, multi-step approach but doesn't specify scenarios where this is preferred over simpler one-shot analysis tools or other siblings. Usage context is only vaguely implied by 'step-by-step 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?

No annotations are provided, so the description carries full burden for behavioral disclosure. It mentions 'set and monitor' but doesn't explain what happens when budgets are exceeded (e.g., whether sessions are terminated, warnings issued), whether changes are persistent, what permissions are required, or rate limits. For a tool that controls critical resources (cost, tokens), this lack of operational detail is a significant gap.

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 immediately conveys the core function. Every word earns its place with no redundancy or fluff. It's appropriately sized for the tool's complexity and front-loaded with 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 tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects (what monitoring entails, consequences of exceeding budgets), return values, or error conditions. Given the potential impact of budget controls, more context about how the tool operates is needed for safe and effective 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 parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond implying budget management involves tokens, cost, and duration. It doesn't explain parameter relationships (e.g., how 'action' values affect other parameters) or provide usage examples. 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 tool's purpose as 'Set and monitor conversation budgets for cost and token control', which includes specific verbs ('set', 'monitor') and resources ('budgets', 'cost', 'tokens'). It distinguishes from sibling tools by focusing on budget management rather than code analysis, debugging, or other functions. However, it doesn't explicitly differentiate from all siblings beyond the general domain difference.

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, appropriate contexts, or exclusions. With many sibling tools available (e.g., ultra-session, ultra-plan), there's no indication of when budget management should be prioritized over other operations.

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 'critical thinking' and 'prevent reflexive agreement,' which imply analysis and questioning, but doesn't detail aspects like response format, error handling, rate limits, or authentication needs. For a tool with no annotations, 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 a single, efficient sentence that front-loads the core purpose without unnecessary details. It's appropriately sized and wastes no words, making it easy to understand quickly.

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 (a critical analysis tool with 4 parameters, no annotations, and no output schema), the description is incomplete. It lacks information on behavioral traits, output expectations, and differentiation from siblings, making it insufficient for an agent to fully understand how to use this tool 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?

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds no specific meaning beyond the schema, such as examples or usage tips for parameters like 'prompt' or 'provider.' With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't 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: 'Challenges a statement or assumption with critical thinking to prevent reflexive agreement.' It specifies the action (challenges with critical thinking) and the resource (statement/assumption), but doesn't explicitly differentiate it from sibling tools like 'challenge' or 'ultra-analyze' which might have similar functions.

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 'challenge' or 'ultra-analyze' among the siblings. It states what the tool does but offers no context about appropriate scenarios, exclusions, or comparisons with other tools.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'context revival across interactions,' which implies persistence, but doesn't detail how context is stored, retrieved, or managed (e.g., session lifetime, data privacy, or error handling). For a tool with 5 parameters and no annotations, this is a significant gap, as it lacks information on permissions, rate limits, or operational constraints.

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: a single, clear sentence that states the core functionality without unnecessary details. Every word earns its place by defining the tool's purpose efficiently, making it easy for an agent to grasp the intent quickly.

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 (5 parameters, session-based AI interactions) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like how context is handled, what the output looks like, or potential side effects. For a tool that likely involves AI model calls and session management, more context is needed to guide effective 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 all parameters thoroughly. The description adds no additional meaning beyond what the schema provides (e.g., it doesn't explain how 'sessionId' is obtained or what 'context revival' entails in practice). With high schema coverage, the baseline is 3, and the description doesn't compensate further.

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: 'Continue a conversation with context from a previous session, enabling context revival across interactions.' It specifies the verb ('continue') and resource ('conversation'), and distinguishes it from siblings by focusing on session-based continuation rather than analysis, debugging, or other tasks. However, it doesn't explicitly differentiate from 'ultra-session' or other session-related tools, keeping it at a 4 instead of 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 when to choose this over starting a new conversation, using other session tools like 'ultra-session', or applying other AI interaction tools. With no explicit usage context or exclusions, 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'systematic debugging' and 'step-by-step root cause analysis' imply a structured process, it doesn't describe what the tool actually does behaviorally: Does it execute code? Does it analyze logs? Does it require specific permissions? Does it have rate limits? The description lacks concrete behavioral traits needed for an agent to understand how this tool operates.

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 at just 6 words ('Systematic debugging with step-by-step root cause analysis'). It's front-loaded with the core purpose and approach. There's zero wasted language or redundancy, making it highly efficient while still conveying the essential concept.

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 complex tool with 11 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how the step-by-step process works, what 'systematic debugging' entails, or how this differs from simpler debugging tools. The agent lacks crucial context about this tool's operation and expected outcomes.

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%, meaning all 11 parameters are documented in the input schema itself. The description adds no additional parameter semantics beyond what's already in the schema descriptions. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the 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 'Systematic debugging with step-by-step root cause analysis' states the general purpose (debugging) and approach (step-by-step analysis), but it's vague about what specific resources or systems it operates on. It doesn't clearly distinguish this tool from sibling tools like 'debug-issue' or 'investigate', leaving ambiguity about when to choose this particular debugging tool.

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 multiple debugging-related sibling tools (debug-issue, investigate, tracer, ultra-analyze, etc.), there's no indication of what makes 'ultra-debug' distinct or when it's the appropriate choice. No exclusions, prerequisites, or comparative context is mentioned.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate comprehensive documentation' implies a creation/write operation, it doesn't specify whether this is a one-time generation or iterative process, what permissions might be required, whether it modifies existing files, or what happens to the 'findings' parameter. The 'step-by-step workflow' hint suggests iteration but lacks concrete behavioral details.

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

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

The description is a single, efficient sentence that clearly states the core function. It's appropriately sized for a tool with this complexity level. While it could be more specific, there's no wasted language or unnecessary elaboration. The structure is straightforward and front-loaded with the main purpose.

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

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

For a complex tool with 11 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain the iterative nature implied by parameters like 'stepNumber', 'totalSteps', and 'findings', nor does it clarify the relationship between 'task' and 'files'. The description leaves too much behavioral ambiguity for a tool of this complexity with no structured safety or output information.

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 provides no parameter-specific information beyond the general purpose. However, with 100% schema description coverage, all 11 parameters are well-documented in the schema itself. The description doesn't add meaning beyond what the schema provides, but the schema does the heavy lifting, justifying the baseline score of 3.

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

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

The description states the tool 'Generate comprehensive documentation with step-by-step workflow' which provides a clear verb ('Generate') and resource ('documentation'), but it's somewhat vague about what specifically is being documented. It doesn't distinguish from sibling tools like 'generate-docs' or 'ultra-analyze' that might have overlapping functionality. The description is adequate but lacks specificity about scope and differentiation.

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

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'generate-docs', 'analyze-code', and 'research' available, there's no indication of what makes this tool distinct or when it should be preferred. The description implies a workflow approach but doesn't specify use cases 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'multi-step feature planning with revisions and branches' which suggests iterative workflow capabilities, but doesn't describe what the tool actually produces (e.g., a plan document, structured output), how revisions/branches are managed, whether it's stateful, or any limitations. The description is too vague to understand the tool's actual 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 a single, efficient phrase that communicates the core concept without unnecessary words. However, it's arguably too concise given the tool's complexity - a 14-parameter planning tool with workflow capabilities might benefit from slightly more elaboration about what 'planning' entails.

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 complex tool with 14 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool produces, how the multi-step workflow operates, what 'revisions and branches' mean in practice, or how this differs from similar planning tools. The combination of complexity and lack of structured documentation makes the current description inadequate.

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 14 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation through the schema alone, though the description contributes nothing extra.

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 'Multi-step feature planning with revisions and branches' states a general purpose but lacks specificity about what the tool actually does (e.g., generates plans, modifies existing ones). It distinguishes from some siblings like 'analyze-code' or 'debug-issue' by focusing on planning, but doesn't clearly differentiate from 'plan-feature' or 'planner' which appear to serve similar functions.

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

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

No guidance is provided on when to use this tool versus alternatives like 'plan-feature' or 'planner' in the sibling list. The description implies a multi-step workflow with revisions and branches, but doesn't specify appropriate contexts, prerequisites, or exclusions for its use.

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. While 'Comprehensive code review' implies a read-only analysis operation, it doesn't disclose behavioral traits like whether it modifies code, requires authentication, has rate limits, returns structured findings, or handles pagination. The mention of 'step-by-step workflow analysis' hints at iterative behavior but lacks specifics.

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. Every word earns its place by conveying comprehensiveness and workflow analysis. However, it could be slightly more structured by explicitly mentioning key capabilities or limitations.

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 complex tool with 12 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain the iterative nature implied by workflow parameters, what 'comprehensive' entails, how findings are returned, or any prerequisites. The agent would struggle to use this effectively without trial and error.

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 12 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, such as explaining relationships between parameters like 'stepNumber' and 'totalSteps' or how 'findings' accumulates. Baseline 3 is appropriate when 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 tool performs 'Comprehensive code review with step-by-step workflow analysis,' which is a specific verb+resource combination. It distinguishes from obvious siblings like 'review-code' by emphasizing comprehensiveness and workflow analysis, though it doesn't explicitly contrast with all similar tools like 'analyze-code' or 'secaudit'.

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 multiple sibling tools like 'review-code,' 'analyze-code,' 'secaudit,' and 'ultra-analyze,' there's no indication of what makes 'ultra-review' distinct or when it's preferred over other code analysis tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It mentions 'persistent context and memory' which hints at stateful behavior, but doesn't clarify important aspects like whether sessions are user-specific, how long they persist, what permissions are required, or what happens when sessions are archived vs deleted.

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 at just 7 words, front-loaded with the core purpose, and contains no wasted language. Every word contributes to understanding the tool's function.

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 tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what a 'session' entails, what 'persistent context and memory' means operationally, or what the tool returns. The lack of output schema means the description should ideally provide some indication of return values.

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

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

The description provides no parameter-specific information beyond what's already in the schema. However, with 100% schema description coverage and clear enum values for the 'action' and 'status' parameters, the schema does most of the work. The description doesn't add meaningful context about how these parameters interact or what the different actions actually do.

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 as managing conversation sessions for persistent context and memory, which is a specific verb+resource combination. However, it doesn't differentiate this tool from its many sibling tools on the server, particularly other 'ultra-' prefixed tools that might also manage conversation aspects.

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 25 sibling tools including several 'ultra-' prefixed tools (ultra-analyze, ultra-challenge, ultra-continuation, etc.), there's no indication of how this session management tool relates to or differs from those other tools.

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