spotify_track_getGet a Spotify track by id or track URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Optional Spotify track URL for the request. | |
| trackId | No | Optional Spotify track id for the request. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds little beyond the obvious 'get' operation; it does not disclose any edge cases or response behavior, but given the tool's simplicity, the annotations suffice. No contradiction.
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, information-dense sentence with no filler. It front-loads the purpose and identification method, achieving maximum clarity with zero waste.
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 simple, read-only get operation with complete parameter documentation and clear annotations, the description covers all essential information. The absence of an output schema is acceptable as the purpose is self-evident.
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 documents both parameters with descriptions at 100% coverage, so the baseline is 3. The description's mention of 'by id or track URL' adds only slight clarification that the two are alternatives, which is not explicitly stated in the schema, but adds minimal value.
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 clear verb ('Get'), a specific resource ('Spotify track'), and the exact method of identification ('by id or track URL'). This distinguishes it from sibling tools like spotify_album_get and spotify_artist_get without any ambiguity.
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 clearly communicates that the tool is for fetching a track and specifies the two accepted identifiers. It does not explicitly name alternatives or provide 'when not to use' guidance, but the context is unambiguous given the tool's name and the sibling list.
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.