Your Spotify MCP Server

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

With no annotations provided, the description carries the full burden. It discloses the return data structure (play count, listening time, etc.), which is helpful, but omits behavioral traits like rate limits, authentication needs, data freshness, or error handling. The description doesn't contradict annotations (none exist), but could be more comprehensive for a stats tool.

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 well-structured and front-loaded with the core purpose, followed by a bulleted list of return data and relevant examples. Every sentence adds value without redundancy, making it efficient and easy to parse for an AI agent.

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?

Given the tool's moderate complexity (single parameter, no output schema, no annotations), the description is largely complete—it explains what the tool does and what data it returns. However, it could improve by addressing missing behavioral context (e.g., data sources, limitations) to fully compensate for the lack of annotations and output schema.

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%, with the parameter 'artist_id' fully documented in the schema (Spotify ID/URI). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation rules, meeting the baseline for high schema coverage.

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 specific action ('Get detailed listening statistics') and resource ('for a specific artist from your listening history'), distinguishing it from siblings like get_top_artists (aggregate list) or get_track_stats (track-level). The examples reinforce this specificity by showing artist-focused queries.

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 implies usage context through examples (e.g., 'How much have I listened to Radiohead?'), suggesting it's for personal artist analytics. However, it lacks explicit guidance on when to use alternatives like get_artist_rank (ranking vs. stats) or get_top_tracks (aggregate vs. artist-specific), leaving some ambiguity.

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