Analyze listening overlap between multiple Your Spotify users. Find songs that multiple users share in common - perfect for: - Creating collaborative playlists - Road trip music everyone enjoys - Party playlists where everyone knows the songs - Understanding shared music tastes with friends Two a...
AI agents call analyze_affinity to retrieve information from Your Spotify MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
analyze_affinity performs data retrieval and comparison across multiple users' listening histories to identify shared songs. This is a read-only operation that queries existing data and returns analytical results. There are no side effects, data modifications, deletions, or external executions involved.
From the tool's definition Tool description states it 'Analyze[s] listening overlap' and 'Find[s] songs that multiple users share in common' - these are query and analysis operations that retrieve and compare existing listening history data without modifying, deleting, or executing…
Attacks that exploit this kind of access
Analyze listening overlap between multiple Your Spotify users. Find songs that multiple users share in common - perfect for: - Creating collaborative playlists - Road trip music everyone enjoys - Party playlists where everyone knows the songs - Understanding shared music tastes with friends Two analysis modes: - minima: Songs EVERYONE has listened to (highest overlap) - Good for:. It is categorised as a Read tool in the Your Spotify MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Your Spotify MCP Server MCP server in PolicyLayer and add a rule for analyze_affinity: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Your Spotify MCP Server. Nothing to install.
analyze_affinity is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the analyze_affinity rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for analyze_affinity. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
analyze_affinity is provided by the Your Spotify MCP Server MCP server (pentafive/your-spotify-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →