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tianmu

Perplexica MCP Server

by tianmu

search_youtube

Search YouTube videos by entering a query. Returns AI-processed results with sources in formatted text or raw JSON.

Instructions

Search YouTube videos using Perplexica.

Args: query: The YouTube search query chat_provider: Chat model provider (optional, uses env config if not provided) chat_model: Specific chat model to use (optional, uses env config if not provided) optimization_mode: Speed vs quality tradeoff (optional, uses env config if not provided) output_format: Output format - "formatted" for human-readable text or "json" for raw JSON

Returns: Formatted text with AI response and sources, or JSON if output_format="json"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
chat_providerNo
chat_modelNo
optimization_modeNo
output_formatNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavior. It explains return formats (formatted or JSON) but omits details about side effects, authentication needs, rate limits, or how Perplexica integrates. This is insufficient for a search tool that relies on external APIs.

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

Conciseness3/5

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

The description is front-loaded with the purpose, but the Args/Returns structure is verbose and slightly repetitive. It could be more concise by integrating parameter descriptions inline.

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

Completeness3/5

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 details (though output schema exists), the description covers the return format and parameters adequately. However, it does not explain the 'Perplexica' context or any behavioral constraints, leaving gaps for a complete understanding.

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

Parameters4/5

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

The schema has 0% description coverage, but the description's Args section adds meaningful explanations for each parameter (e.g., 'The YouTube search query,' 'Chat model provider (optional, uses env config if not provided)'). This compensates for the schema gaps.

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

Purpose4/5

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

The description clearly states 'Search YouTube videos using Perplexica,' indicating a specific verb and resource. However, it does not differentiate this tool from sibling tools like search_web or search_reddit, which would help clarify unique purpose.

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

Usage Guidelines2/5

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, nor does it mention prerequisites or exclusions. It only lists parameters but lacks contextual usage advice.

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

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