Your Spotify MCP Server

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

With no annotations provided, the description carries full burden and does well by explaining the two analysis modes and their scoring logic (lowest listener's play count vs. average play count). It could improve by mentioning potential limitations like data freshness or privacy considerations, but covers core behavioral aspects adequately.

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 with clear sections (purpose, use cases, analysis modes, examples) and uses bullet points effectively. While comprehensive, it could be slightly more concise by integrating some explanatory text more tightly, but every sentence adds meaningful context.

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 tool with 5 parameters, 100% schema coverage, and no output schema, the description provides excellent context about what the tool does, when to use it, and how different modes work. The main gap is lack of information about return format or result structure, which would be helpful given no output schema exists.

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%, so the schema already documents all parameters thoroughly. The description adds value by explaining the semantic difference between 'minima' and 'average' modes beyond the enum values, but doesn't provide additional context for other parameters like date filters or user_ids beyond what's in the schema.

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 analyzes listening overlap between multiple Spotify users to find shared songs. It specifies the verb 'analyze' and resource 'listening overlap', distinguishing it from siblings like 'analyze_listening_patterns' or 'compare_listening_periods' which likely focus on different aspects of user data.

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 explicitly provides when-to-use guidance with four specific use cases (collaborative playlists, road trips, party playlists, understanding shared tastes) and two analysis modes with clear recommendations ('minima' for songs everyone knows, 'average' for songs someone will love). Example queries further illustrate appropriate contexts.

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