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shell-as-mcp

by meomeo-dev

ytdlp__search_videos

Search YouTube videos by keyword using yt-dlp, with options for result count, pagination, upload date filtering, and output as summary or JSON.

Instructions

Search YouTube videos by keyword using yt-dlp and return results as summary or JSON. @remarks Uses ytsearch to query YouTube; supports pagination via offset and date filtering. @param query Search keywords (must not contain newlines). @param maxResults Number of results to return (1-50, default 10). @param offset Pagination offset (default 0). @param response_format Output format: "summary" (default) or "json". @param uploadDateFilter Filter by upload date: "today", "week", "month", or "year". @param cookies Optional path to a Netscape-format cookies file. @param proxy Optional proxy address (e.g. socks5://127.0.0.1:1080). @param maxRetries Optional retry count after failures, capped at 2 (default: 2). @param __mcp_response_mode Optional response mode: content (default) or structuredContent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
proxyNo
queryYes
offsetNo
cookiesNo
maxResultsNo
maxRetriesNo
response_formatNo
uploadDateFilterNo
__mcp_response_modeNocontent
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses the use of ytsearch, supports pagination, and explains output format choices. It also documents optional cookie/proxy/retry behavior. However, it does not mention rate limits or failure modes, so it's not perfect.

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

Conciseness5/5

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

The description is well-structured with @remarks and @param blocks. Every line carries useful information without redundancy. The length is justified given the tool has 9 parameters and needs to clarify each.

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

Completeness4/5

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

Given the tool's complexity (9 parameters, no output schema, no annotations), the description adequately covers purpose, parameters, and usage behavior. It doesn't detail the exact shape of the returned results, but the 'summary or JSON' note and pagination details are sufficient for an agent to invoke it correctly.

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

Parameters5/5

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

Schema description coverage is 0%, but the @param lines in the description fully document all 9 parameters with constraints, defaults, and valid values (e.g., maxResults 1-50, uploadDateFilter enum, maxRetries cap). This completely compensates for the schema's lack of descriptions.

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

Purpose5/5

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

The description clearly states the tool searches YouTube videos by keyword using yt-dlp and returns results in summary or JSON format. This specifically differentiates it from sibling tools like ytdlp__download_video or ytdlp__get_video_metadata.

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

Usage Guidelines4/5

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

The description implies use for search tasks and mentions pagination and date filtering, but it does not explicitly state when to use this tool over alternatives. The context is clear, yet exclusions or alternative references are absent.

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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