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SoundCloud MCP Server

SoundCloud MCP Server

by jamiew

Get an artist's likes

get_user_likes
Read-only

List liked tracks, playlists, or stations for a SoundCloud user, with options for result limit and detailed fields.

Instructions

List tracks, playlists, or stations a user has liked.

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": {
      "userId": "<userId>"
    },
    "name": "get_user_likes"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindNotracks
limitNo
userIdYes
verboseNoReturn every field SoundCloud sends, such as descriptions, not the compact summary

Output Schema

NameRequiredDescriptionDefault
next_hrefNoNext page URL; pass to next_page
collectionYesPage items

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.1.2

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds nothing beyond that: it does not mention default kind (tracks), default limit (50) or the 200 ceiling, whether pagination/next_page applies across results, or what the verbose flag changes about payload shape. For a read tool with an output schema, some gaps are tolerable, but it gives no behavioral context at all.

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

Conciseness4/5

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

A single, front-loaded sentence with no filler or redundancy. It is efficient, though its brevity is achieved by omitting rather than by tight compression.

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

Completeness2/5

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

Four parameters at 25% schema coverage, an enum kind, and no output schema of its own semantics explained; the annotations only cover read-only and open-world hints. The description leaves the agent guessing about defaults, paging, and the my-likes-vs-user-likes distinction.

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

Parameters2/5

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

Schema description coverage is only 25% — only the verbose parameter is documented in-schema. The description lists the three kinds conceptually but says nothing about the kind default, the limit default/maximum, or the accepted userId types (string or number). With such low coverage, the description needed to compensate and does not.

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?

The description states a specific verb ('List') and concrete resources ('tracks, playlists, or stations') liked by a user, which matches the kind enum and distinguishes it from write siblings like like_track. Differentiation from near-siblings get_my_likes and get_user_reposts is only implicit (via the 'userId' framing), so it falls short of 5.

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

Usage Guidelines3/5

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

Usage is implied by the resource framing: retrieve a given user's liked items. There is no explicit when-to-use or when-not-to-use guidance, and no pointer to get_my_likes (for the authenticated user) versus get_user_likes (for an arbitrary user), which is precisely the ambiguity an agent would hit.

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