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lyricmood-mcp

lyricmood-mcp

by yigitates17
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Search Track

search_track

Searches Spotify for tracks by free text, field filters, or a Spotify URL/URI/ID and returns compact metadata with IDs for use in other lyric and mood tools.

Instructions

Search Spotify for tracks. Returns compact track info (id, title, artists, album, year, duration, isrc). Use the returned id to refer to a track in other tools. A Spotify link or ID returns exactly that track.

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": {
      "query": "<query>"
    },
    "name": "search_track"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax results (1-10).
queryYesFree text like "nemo nightwish", Spotify field filters like "artist:Nightwish track:Nemo", or a Spotify track URL/URI/ID.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the full burden. It does disclose the return payload and the exact-match behavior for URL/URI/ID inputs, which is genuinely useful, but it says nothing about authentication, rate limits, or what happens when nothing matches.

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?

Three short sentences, front-loaded with the action, with no redundant filler. Every sentence contributes something (purpose, output shape, ID reuse, exact-match case), though the output-field list could be trimmed against an output schema.

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?

For a two-parameter read-only search with no output schema, the description covers purpose, query flexibility, return fields, and the exact-match edge case. Only error/empty-result behavior and rate-limit context are absent.

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 100% schema description coverage, both parameters are already fully documented in the schema, including the limit range and the accepted query formats. The description's note about links/IDs largely restates the schema, adding little new meaning.

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 names a specific verb and resource ('Search Spotify for tracks') and enumerates the returned fields, making it clearly distinct from retrieval siblings like get_saved_tracks or get_playlist_tracks. It stops short of explicitly contrasting with get_track_profile or get_my_top_tracks, so sibling differentiation is only implied.

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?

It gives useful downstream guidance ('Use the returned `id` to refer to a track in other tools') and describes accepted query forms, but never states when to prefer this tool over the many other track-retrieval siblings, nor any exclusion conditions.

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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