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 'real-time web search' and 'synthesized from multiple online sources,' which adds some context about data sources and synthesis. However, it lacks details on rate limits, authentication needs, response format, or potential side effects, which are important for a tool performing external queries.

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

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

The description is front-loaded and efficient, using two concise sentences that directly convey the tool's purpose and output. Every sentence earns its place by specifying the action, resource, and result without unnecessary 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 no annotations and no output schema, the description is moderately complete for a query tool. It covers the basic purpose and data sources but lacks details on behavioral traits like error handling, response structure, or operational constraints. This leaves gaps in understanding how to effectively invoke and interpret 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%, with clear descriptions for both parameters (query and model). The description adds no additional parameter semantics beyond what the schema provides, such as query formatting tips or model selection guidance. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the specific action ('Ask Perplexity AI a question with real-time web search') and resource ('Perplexity AI'), distinguishing it from siblings like perplexity_list_models (listing models), perplexity_login (authentication), and perplexity_status (status checking). It specifies the verb+resource+scope combination precisely.

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

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

The description implies usage context ('Ask Perplexity AI a question with real-time web search'), suggesting this tool is for querying with web search capabilities. However, it doesn't explicitly state when to use this vs. alternatives (e.g., if other tools exist for non-search queries) or provide exclusions, leaving some ambiguity about optimal use cases.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('List all available models') but doesn't describe traits like whether it's a read-only operation, requires authentication (implied by 'perplexity_login' sibling), rate limits, or output format. For a tool with zero annotation coverage, this leaves significant behavioral gaps, scoring a 2.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It's front-loaded with the core action and resource, making it highly concise and well-structured, earning a 5.

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) and lack of annotations, the description is adequate but incomplete. It covers the basic purpose but misses contextual details like authentication needs (suggested by 'perplexity_login'), typical use cases, or output expectations. This is minimally viable but has clear gaps, scoring a 3.

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 input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, as there are none to document. This meets the baseline of 4 for zero parameters, as it appropriately doesn't discuss non-existent inputs.

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 Perplexity AI models'), making the purpose unambiguous. It distinguishes from siblings like 'perplexity_ask' (querying models) and 'perplexity_status' (checking service status), though it doesn't explicitly mention these distinctions. The description is specific but lacks explicit sibling differentiation, warranting a 4.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios like selecting a model for use with 'perplexity_ask', checking model availability before queries, or comparing it to 'perplexity_status' for service health. Without any usage context or exclusions, this is minimal guidance, scoring 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. It effectively describes key behaviors: opening a browser window (external interaction), requiring user completion of login (manual step), and automatically saving cookies (outcome). However, it lacks details on error handling, timeout behavior, or what happens if login fails, which prevents a perfect score.

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

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

The description is front-loaded and efficiently structured in two sentences: the first states the core action and outcome, the second clarifies the user's role. Every sentence earns its place by adding critical information (automation steps and manual requirement) with zero waste.

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

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

Given the tool's complexity (involving browser automation and authentication), no annotations, and no output schema, the description is largely complete. It covers the purpose, process, and outcome. However, it omits details like where cookies are saved or how to verify success, which would enhance completeness for this type of tool.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately adds context about the tool's operation (browser-based login flow) without redundant parameter details. A baseline of 4 is applied since no parameters exist, and the description provides useful operational 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 with specific verbs ('Opens a browser window', 'log in', 'saves cookies') and resources ('Perplexity'), distinguishing it from sibling tools like perplexity_ask (querying) and perplexity_list_models (listing models). It explicitly describes the authentication flow, which is unique among the siblings.

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

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

The description provides explicit guidance on when to use this tool: for logging into Perplexity to obtain cookies. It implicitly distinguishes from alternatives by focusing on authentication (vs. querying with perplexity_ask or checking status with perplexity_status). The instruction 'User must complete login in the browser' clarifies the required user interaction, making usage context clear.

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 what the tool does (checking status) but doesn't describe traits like whether it's read-only, what the output format might be (e.g., success/failure, cookie validity), or any side effects (e.g., logging). This is a significant gap for a tool with zero annotation coverage.

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

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

The description is a single, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'status' entails (e.g., configuration validity, cookie expiration), the return format, or potential error conditions. For a tool that likely involves system state checks, this leaves too much ambiguity 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?

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter details, so it meets the baseline of 4 for tools with no parameters, as there's nothing to compensate for.

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

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

The description clearly states the tool's purpose with a specific verb ('Check') and resource ('Perplexity MCP configuration and cookies'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'perplexity_login' (which might also involve configuration/cookie status), so it doesn't reach the highest score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'perplexity_login' (for authentication) or 'perplexity_ask' (for queries). It implies usage for status checking but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer when this is appropriate.

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