Search YouTube channels by keyword. Returns channel name, description, subscriber count, and thumbnail.
| Name | Required | Description | Default |
|---|---|---|---|
| pages | No | Number of pages to fetch, 1-10 (default: 1) | |
| query | Yes | Search keyword (max 500 characters) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavioral traits. It does state the return fields, which gives some insight into the output structure. However, it does not explicitly mention that the operation is read-only, any pagination behavior (beyond what the schema says via 'pages'), rate limits, or potential error conditions. The description is adequate but not rich in behavioral transparency.
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 extremely concise, consisting of two short sentences. The first sentence states the core function and the second lists the returned data. Every word serves a purpose, and the structure is front-loaded with the primary action. It is an efficient and clear description without any filler.
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?
For a relatively simple search tool, the description is almost complete. It explains the purpose and the return fields, while the schema handles the parameters. There is no output schema, so the description's mention of return fields fills that gap. However, it lacks any mention of result ordering, default behavior, or explicit differences from sibling search tools, but these are not critical for a basic channel search.
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 already provides complete descriptions for both parameters (query and pages), so the description adds little extra meaning beyond what the schema conveys. It reinforces that 'query' is the keyword, but it does not elaborate on usage context or edge cases for the parameters. With 100% schema coverage, the baseline of 3 is appropriate.
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 function: 'Search YouTube channels by keyword' with a specific verb, resource, and method. It distinguishes from sibling tools by specifying 'channels' rather than videos or general web search, and it lists the key return fields (channel name, description, subscriber count, and thumbnail). This makes the purpose immediately obvious.
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 usage is implied by the description: use this when you need to discover YouTube channels based on a keyword. However, there is no explicit guidance on when not to use this tool or alternatives such as search_youtube (for videos) or youtube_channel_details (for a specific channel's details). The description provides a clear context but lacks explicit exclusions or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.
The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.
74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.
The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.
