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Spotify Data MCP Server

Spotify Data MCP Server

by eduair94
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Track play counts

get_track_play_count
Read-onlyIdempotent

Retrieve lifetime Spotify play counts for up to 50 tracks in one call, along with track names, artists, and release dates to compare streams.

Instructions

Lifetime Spotify play counts (streams) for up to 50 tracks in one call, with track name, artists and release date. The fastest way to compare streams. A play count of 0 means fewer than 1,000 plays.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tracks": "<tracks>"
    },
    "name": "get_track_play_count"
  }
}

Input Schema

NameRequiredDescriptionDefault
tracksYesSpotify track ids, spotify:track:<id> URIs or open.spotify.com/track/<id> URLs (1-50).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, openWorld, so the safety profile is covered. The description adds genuine behavioral context beyond that: the batch limit, the returned fields, and the important data-semantics caveat that '0 means fewer than 1,000 plays' — which prevents misreading a censored value as literally zero streams. It stops short of describing error behavior for unknown tracks or whether counts are approximate above a threshold.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences: capability and scope first, positioning second, data caveat last. No filler, and the most decision-relevant information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, but the description compensates by naming the returned fields (play count, track name, artists, release date). For a single-parameter read tool that is close to sufficient; only error/partial-result behavior for invalid or unknown track ids is left unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With one parameter and 100% schema description coverage, the schema already documents that tracks accepts 1-50 ids/URIs/URLs. The description only echoes the '50 tracks' bound and adds no accepted-format detail beyond what the schema states, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb and resource ('Lifetime Spotify play counts (streams)') plus the batch scope (up to 50 tracks in one call), and lists the fields returned. It does not name any sibling such as get_track_insights or get_tracks, so the differentiation from those is only implied by 'fastest way to compare streams'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'The fastest way to compare streams' gives a clear usage context: reach for this when comparing multiple tracks at once. There is no explicit when-not guidance and no named alternative for single-track or detailed-analysis needs.

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

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