twitch_profile_schedule_getGet the stream schedule for a Twitch channel by handle.
| Name | Required | Description | Default |
|---|---|---|---|
| handle | Yes | Twitch handle to look up, with or without a leading @. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description doesn't need to restate that. The description adds only that it returns the schedule, which is implicit in the tool name. No additional behavioral details (pagination, rate limits, data freshness) are provided, but for a simple read-only getter with annotations covering the safety profile, this is acceptable.
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 with zero filler. It conveys the core purpose and input method immediately, 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?
For a one-parameter read-only getter with no output schema, the description is sufficiently complete: it tells what data is returned (schedule) and how to specify the channel. The lack of explicit return format is minor given the tool's simplicity and the existence of similar sibling tools that follow the same pattern.
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% (only 'handle' is fully described in the input schema with format and leading '@' note). The tool description adds no extra meaning beyond the schema, so the baseline of 3 applies.
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 states a specific verb ('Get'), resource ('stream schedule for a Twitch channel'), and the lookup method ('by handle'). It clearly distinguishes from sibling tools like twitch_profile_get and twitch_profile_videos_list by naming the exact data being fetched.
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 indicates the input format (handle) but does not explicitly contrast with alternatives (e.g., when to use this vs. twitch_profile_get). While the tool name makes the purpose obvious, the description itself gives no explicit when-to-use guidance, though the single-sentence clarity is adequate for a simple getter.
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.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.