Perplexity MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool performs searches with real-time web access, includes citations to sources, and is optimized for current or authoritative information. However, it lacks details on rate limits, authentication needs, or error handling, which are minor 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 appropriately sized and front-loaded, starting with the core functionality and followed by usage guidelines. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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 (search with AI and web integration) and lack of annotations or output schema, the description is mostly complete. It covers purpose, usage, and behavioral aspects well, but could benefit from mentioning response format or potential limitations to fully compensate for the missing structured data.

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

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

The schema description coverage is 100%, so the input schema already documents the 'content' parameter as a string for the user query. The description does not add any additional meaning or context about parameters beyond what the schema provides, such as formatting examples or constraints, resulting in a baseline score.

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 ('Ask questions', 'get AI-powered answers') and resources ('Perplexity AI', 'real-time web search'). It explicitly distinguishes what the tool does by mentioning its unique features like citations and web-sourced information, even without siblings for comparison.

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, listing specific scenarios such as 'current information, facts, research, news' and 'any query that benefits from up-to-date web sources'. It includes a 'Best for' section with detailed examples like current events and technical documentation lookups, offering clear context for usage.

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