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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' analytics and metrics, implying a read-only operation, but doesn't disclose any behavioral traits such as authentication requirements, rate limits, data freshness, or potential side effects (e.g., if it triggers data collection). The description is too vague to inform the agent about how the tool behaves beyond its basic purpose.

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

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

The description is a single, efficient sentence: 'Get detailed analytics and performance metrics for the Perplexity MCP server.' It is front-loaded with the core purpose, uses clear language, and contains no redundant or unnecessary information. Every word earns its place by specifying what is retrieved and for what scope.

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 (0 parameters, no output schema, no annotations), the description is minimally complete. It states what the tool does but lacks details on usage guidelines, behavioral transparency, or output expectations. While it covers the basic purpose adequately, it doesn't provide enough context for an agent to fully understand when and how to use this tool effectively, especially compared to siblings.

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 the schema description coverage is 100% (since there are no parameters to describe). In such cases, the baseline score is 4, as there are no parameters for the description to compensate for or add meaning beyond the schema. The description doesn't need to discuss parameters, and it appropriately avoids doing so.

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

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

The description clearly states the tool's purpose: 'Get detailed analytics and performance metrics for the Perplexity MCP server.' It uses a specific verb ('Get') and identifies the resource ('analytics and performance metrics') with a clear scope ('for the Perplexity MCP server'). However, it doesn't explicitly differentiate from sibling tools like perplexity_ask_pro or perplexity_models, which might also provide some metrics or data.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for usage, or exclusions. For example, it doesn't clarify if this is for monitoring server health, debugging, or general reporting, or how it differs from perplexity_ask_pro which might handle queries. This lack of guidance leaves the agent with minimal context for tool selection.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'caching' and 'full structured responses,' which hints at performance and output format, but lacks critical details like rate limits, authentication requirements, error handling, or whether this is a read-only or mutating operation. For a complex API tool with multiple parameters, 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 front-loads key features (CORRECT 2025 models, structured responses, caching, advanced features) and ends with supported capabilities. It avoids redundancy, though it could be slightly more structured by separating features from use cases.

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 (4 parameters with nested objects, no output schema, and no annotations), the description is inadequate. It doesn't explain return values, error conditions, or how the 'advanced features' map to the options parameter. For a sophisticated API tool, 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 minimal value beyond the schema by vaguely referencing 'proper parameters' and 'advanced features,' but doesn't explain parameter interactions, default behaviors, or practical usage examples. This meets the baseline for high schema coverage.

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

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

The description clearly states this is an 'Ultra-Pro Perplexity API' that 'supports search, research, reasoning, and offline models with proper parameters,' which specifies the verb (API interaction) and resource (Perplexity models). However, it doesn't explicitly differentiate from sibling tools like perplexity_analytics or perplexity_models, which likely serve different purposes (analytics vs. model queries vs. this general API 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 mentions 'advanced features' and lists supported capabilities (search, research, reasoning, offline models), but provides no explicit guidance on when to use this tool versus alternatives like perplexity_analytics or perplexity_models. There's no mention of prerequisites, exclusions, or specific contexts where this tool is preferred over others.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool clears a cache to force fresh API calls, which implies a mutation operation, but doesn't disclose potential side effects (e.g., performance impact, data loss), permissions required, or rate limits. This leaves significant gaps in understanding the tool's 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 directly states the tool's purpose without any wasted words. It's front-loaded and appropriately sized for a simple tool with no parameters.

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 adequate but incomplete. It explains what the tool does but lacks details on behavioral aspects like side effects or usage context, which are important for a cache-clearing operation that could impact system performance.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose. This meets the baseline for tools with no parameters.

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

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

The description clearly states the action ('Clear') and the target resource ('response cache'), with the purpose being to 'force fresh API calls'. It's specific about what the tool does, though it doesn't explicitly differentiate from sibling tools like perplexity_analytics or perplexity_models, which appear to be unrelated operations.

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

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

The description implies usage when fresh API calls are needed, but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites. There's no mention of when-not-to-use scenarios or how it relates to sibling tools, leaving the agent to infer context.

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

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

No annotations are provided, so the description carries full burden. It mentions 'List' and 'descriptions', implying a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, returns structured data, or handles errors. The '(2025 correct models)' hints at up-to-date information but lacks operational 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 front-loads the core purpose ('List available Perplexity models with descriptions') and adds a clarifying note ('2025 correct models'). Every word earns its place, with no redundancy or waste, making it highly concise and well-structured.

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

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

Given the tool's low complexity (0 parameters, no output schema, no annotations), the description is adequate but has gaps. It covers the basic purpose but lacks behavioral context (e.g., read-only nature, response format) and usage guidelines. Without annotations or output schema, more detail on what the list includes would improve completeness.

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

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

The tool has 0 parameters, with schema description coverage at 100%. The description doesn't need to add parameter semantics, as there are none to document. Baseline for 0 parameters is 4, as the description appropriately focuses on the tool's purpose without unnecessary parameter details.

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

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

The description clearly states the tool's purpose: 'List available Perplexity models with descriptions'. It specifies the verb ('List'), resource ('Perplexity models'), and scope ('with descriptions'), though it doesn't explicitly differentiate from sibling tools like perplexity_analytics or perplexity_ask_pro. The '(2025 correct models)' adds temporal accuracy but doesn't enhance core purpose clarity.

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 (e.g., perplexity_ask_pro for querying models) or suggest scenarios where listing models is appropriate, such as before selecting one for a task. Usage is implied by the purpose but not explicitly stated.

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