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Glama

Spotify

by io.github.pipeworx-io

Get Audio Features

get_audio_features
Read-onlyIdempotent

Audio features for a track: tempo (BPM), key, mode (major/minor), time_signature, energy, danceability, valence, acousticness, instrumentalness, liveness, speechiness, loudness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
track_idYesSpotify track ID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoTrack ID
keyNoKey 0-11 (C=0 to B=11)
uriNoSpotify URI
modeNoMode: 1=major, 0=minor
tempoNoTempo in BPM
energyNoEnergy 0-1 (intensity/activity)
valenceNoValence 0-1 (musicality positivity)
livenessNoLiveness 0-1 (audience presence)
loudnessNoLoudness in dB
track_hrefNoTrack API endpoint
speechinessNoSpeechiness 0-1 (spoken words)
acousticnessNoAcousticness 0-1
analysis_urlNoDetailed audio analysis endpoint
danceabilityNoDanceability 0-1
time_signatureNoTime signature (4/4 = 4)
instrumentalnessNoInstrumentalness 0-1 (no vocals)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "track_id": "11dFghVXANMlKmJXsNCQvb"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Audio features for a track: tempo, key, energy, danceability, valence, etc.",
+  "properties": {
+    "acousticness": {
+      "description": "Acousticness 0-1",
+      "type": "number"
+    },
+    "analysis_url": {
+      "description": "Detailed audio analysis endpoint",
+      "type": "string"
+    },
+    "danceability": {
+      "description": "Danceability 0-1",
+      "type": "number"
+    },
+    "energy": {
+      "description": "Energy 0-1 (intensity/activity)",
+      "type": "number"
+    },
+    "id": {
+      "description": "Track ID",
+      "type": "string"
+    },
+    "instrumentalness": {
+      "description": "Instrumentalness 0-1 (no vocals)",
+      "type": "number"
+    },
+    "key": {
+      "description": "Key 0-11 (C=0 to B=11)",
+      "type": "number"
+    },
+    "liveness": {
+      "description": "Liveness 0-1 (audience presence)",
+      "type": "number"
+    },
+    "loudness": {
+      "description": "Loudness in dB",
+      "type": "number"
+    },
+    "mode": {
+      "description": "Mode: 1=major, 0=minor",
+      "type": "number"
+    },
+    "speechiness": {
+      "description": "Speechiness 0-1 (spoken words)",
+      "type": "number"
+    },
+    "tempo": {
+      "description": "Tempo in BPM",
+      "type": "number"
+    },
+    "time_signature": {
+      "description": "Time signature (4/4 = 4)",
+      "type": "number"
+    },
+    "track_href": {
+      "description": "Track API endpoint",
+      "type": "string"
+    },
+    "uri": {
+      "description": "Spotify URI",
+      "type": "string"
+    },
+    "valence": {
+      "description": "Valence 0-1 (musicality positivity)",
+      "type": "number"
+    }
+  },
+  "type": "object"
+}
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare this as read-only, idempotent, and non-destructive, so the safety profile is known. The description adds no extra behavioral context (e.g., errors, authentication, or rate limits) but also does not contradict the annotations.

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?

The description is a single, front-loaded sentence that directly states the tool's purpose and enumerates the returned features. It is concise, with no unnecessary words or repetition.

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 simple tool with one parameter, rich annotations, and an output schema, the description adequately covers the essential information. It could mention limitations or usage scenarios, but the feature list combined with the schema makes it sufficiently complete for an agent.

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?

The schema fully describes the only parameter (track_id as 'Spotify track ID') with 100% coverage, so the baseline is 3. The description does not add any input semantics beyond the schema; it focuses on output features.

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 clearly states the resource (a track's audio features) and lists the specific features (tempo, key, mode, etc.), making the purpose unambiguous. It distinguishes from siblings like get_track implicitly through the unique feature list, though it doesn't explicitly name alternatives.

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 list of audio features: an agent would use this when needing tempo, key, danceability, etc. However, there is no explicit guidance on when to use this tool versus alternatives, nor any mention of when not to use it.

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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TDQS

B3.1/5.0
Disambiguation1/5

The tool set is a chaotic mix of Spotify music tools and an extensive Pipeworx data querying system, with no clear separation. Tools like 'ask_pipeworx', 'ask_pipeworx_grounded', and 'deep_research' have overlapping querying purposes, while unrelated tools from prediction markets and company research further muddy the boundaries. An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency2/5

Naming conventions are inconsistent: Spotify tools follow a verb_noun pattern (e.g., 'get_album', 'search'), while Pipeworx tools use descriptive phrases (e.g., 'ask_pipeworx', 'bet_research'). Additionally, some tools have vague names like 'process' or 'run' that could apply to anything. The lack of a unified naming scheme makes the set feel disjointed.

Tool Count2/5

With 36 tools, the count is high, but the server name 'Spotify' implies a focused music service. The majority of tools are unrelated to Spotify (e.g., Pipeworx queries, Polymarket bets, dependency scanning), making the tool count feel inflated and inappropriate for the stated domain. A more focused set would be far more coherent.

Completeness1/5

If this is a Spotify server, it is severely incomplete: it lacks playlist management, user actions, and recommendation features beyond top tracks. The inclusion of dozens of unrelated tools (financial data, prediction markets, package analysis) suggests the server has no clear purpose, leaving it neither complete for Spotify nor for any other single domain.

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