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AutoEQ MCP Server

AutoEQ MCP Server

by verIdyia

AutoEQ MCP Server

PyPI License: MIT Python 3.10+ Claude Desktop Claude Code claude.ai

pip install autoeq-mcp

An MCP (Model Context Protocol) server that gives AI assistants access to the AutoEQ headphone equalization database — 8,800+ headphones and IEMs with parametric EQ settings, sound signature analysis, and Harman preference scores.

한국어 README

What It Does

Ask your AI assistant things like:

  • "Get me the EQ settings for the HD650"

  • "Compare the HE400se and HD600"

  • "Recommend warm-sounding over-ear headphones"

  • "What are the top-ranked IEMs by Harman score?"

The server automatically analyzes frequency response measurements across 8 bands and classifies each headphone's sound signature (Neutral, Warm, Bright, Dark, V-shaped, etc.).

Related MCP server: Audio Analysis MCP Server

Demo

Headphone comparison with vocal suitability analysis

Comparison demo — HD650 vs DT 990 Pro with per-band chart and vocal recommendation

Finding similar IEMs by sound signature

Similar search demo — finding IEMs with similar tuning using per-band analysis

Tools

Tool

Description

eq_search

Search by name, type (over-ear/in-ear/earbud), sound signature, or measurement source

eq_profile

Get full EQ profile — parametric EQ, fixed band EQ, per-band analysis with visual bars

eq_compare

Side-by-side comparison of two headphones across all frequency bands

eq_recommend

Recommendations by preference (neutral, warm, bright, bass, vocal, fun, analytical)

eq_ranking

Harman headphone listener preference score rankings

eq_targets

List all 61 available target curves (Harman, Diffuse Field, etc.)

eq_sync

Pull latest data from AutoEQ GitHub and rebuild the database

Example Output

# Sennheiser HD 650
- Source: oratory1990
- Type: over-ear
- Harman preference score: 84.0
- Sound signature: Neutral, Harman-like

## Per-band analysis (deviation from target, dB)
  Sub-bass (20-60Hz):   -3.2 dB [·······▓▓▓|··········] sub-bass lacking
  Bass (60-250Hz):      +0.8 dB [··········|··········] close to target
  Mid (500-1kHz):       -0.3 dB [··········|··········] close to target
  Presence (2k-4kHz):   +1.4 dB [··········|▓·········] detail emphasis
  Air (8k-20kHz):       -2.1 dB [········▓▓|··········] closed / lacking air

## Parametric EQ (Preamp: -6.5 dB)
  #  Type        Fc (Hz)      Q  Gain (dB)
  1  LowShelf        105   0.70       +6.5
  2  Peaking        1800   1.20       -2.3
  ...

Installation

Claude Code / Claude Desktop (stdio)

# Install
pip install autoeq-mcp

# Initial database sync (clones AutoEQ repo + builds SQLite DB, ~20s)
autoeq-mcp --sync

# Add to Claude Code
claude mcp add autoeq_mcp -- autoeq-mcp

For Claude Desktop, add to your config file:

{
  "mcpServers": {
    "autoeq": {
      "command": "autoeq-mcp"
    }
  }
}

SSE Mode (Remote / Multi-client)

# Start SSE server
AUTOEQ_MCP_PORT=3008 autoeq-mcp --sse

# With allowed hosts for DNS rebinding protection
AUTOEQ_MCP_ALLOWED_HOSTS="your-domain.com,localhost" autoeq-mcp --sse

From Source

git clone https://github.com/verIdyia/autoeq-mcp
cd autoeq-mcp
pip install -e .
autoeq-mcp --sync

Configuration

All configuration is via environment variables:

Variable

Default

Description

AUTOEQ_DATA_DIR

~/.autoeq-mcp

Directory for repo clone and SQLite DB

AUTOEQ_MCP_PORT

3008

SSE server port

AUTOEQ_MCP_HOST

0.0.0.0

SSE server host

AUTOEQ_MCP_ALLOWED_HOSTS

(none)

Comma-separated allowed hosts for SSE

Data Source

All headphone data comes from AutoEQ by Jaakko Pasanen (MIT License).

  • 8,800+ headphone/IEM profiles

  • 22 measurement sources (oratory1990, crinacle, Rtings, and more)

  • 61 target curves (Harman 2018/2019, Diffuse Field, etc.)

  • 2,300+ Harman preference scores

The database syncs from the AutoEQ GitHub repository. Run eq_sync or autoeq-mcp --sync to update.

How Sound Signatures Work

The server analyzes each headphone's frequency response error (deviation from target) across 8 bands and classifies it:

Signature

Characteristics

Neutral

All bands within ±2 dB of target

Warm

Elevated bass, flat/recessed treble

Bright

Elevated treble, flat/recessed bass

Dark

Recessed treble

V-shaped

Elevated bass + treble, recessed mids

U-shaped

Elevated bass + treble

Bass-heavy

Strongly elevated bass (>3 dB)

Mid-forward

Elevated mids, flat bass/treble

Harman-like

Total deviation < 1.5 dB average

License

MIT — See LICENSE

Available Tools

7 tools
eq_compareA
Read-onlyIdempotent

Compare two headphones band-by-band with sound signature analysis.

Inputs: name1 (required), name2 (required). Returns: result

eq_profileB
Read-onlyIdempotent

Get detailed EQ profile for a headphone. Includes parametric EQ, fixed band EQ, and per-band sound analysis.

Inputs: name (required), source. Returns: result

eq_rankingA
Read-onlyIdempotent

Get headphone rankings by Harman headphone listener preference score.

Inputs: form_factor, limit. Returns: result

eq_recommendA
Read-onlyIdempotent

Recommend headphones based on sound preference and type. Sorted by Harman preference score.

Inputs: preference, form_factor, limit. Returns: result

eq_syncA
Idempotent

Pull latest AutoEQ data from GitHub and rebuild the database. May take a few minutes.

Inputs: no named parameters. Returns: result

eq_targetsA
Read-onlyIdempotent

List all available EQ target curves (Harman, Diffuse Field, etc.).

Inputs: no named parameters. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: compare headphones, get EQ profiles, view rankings, get recommendations, search the database, sync data, and list target curves. There is no overlap in functionality, and the descriptions make the distinctions unambiguous.

    Naming Consistency5/5

    All tool names follow a consistent 'eq_' prefix with a descriptive suffix (e.g., compare, profile, ranking), using snake_case uniformly. This pattern is predictable and enhances readability across the tool set.

    Tool Count5/5

    With 7 tools, the server is well-scoped for its purpose of managing headphone EQ data and recommendations. Each tool serves a unique and necessary function, avoiding bloat while covering core operations like querying, syncing, and analyzing.

    Completeness5/5

    The tool set provides comprehensive coverage for the AutoEQ domain, including data retrieval (search, profile, targets), analysis (compare, ranking), recommendations, and maintenance (sync). There are no obvious gaps, enabling full agent workflows from discovery to implementation.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data

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