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Provides access to a database of over 8,800 headphones and IEMs for equalization settings, sound signature analysis, and Harman preference scores. It enables AI
Provides access to a database of over 8,800 headphones and IEMs for equalization settings, sound signature analysis, and Harman preference scores. It enables AI assistants to search, compare, and recommend headphones based on frequency response measurements and parametric EQ profiles.
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.
Ask your AI assistant things like:
The server automatically analyzes frequency response measurements across 8 bands and classifies each headphone's sound signature (Neutral, Warm, Bright, Dark, V-shaped, etc.).


| 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 |
# 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
...
# 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"
}
}
}
# 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
git clone https://github.com/verIdyia/autoeq-mcp
cd autoeq-mcp
pip install -e .
autoeq-mcp --sync
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 |
All headphone data comes from AutoEQ by Jaakko Pasanen (MIT License).
The database syncs from the AutoEQ GitHub repository. Run eq_sync or autoeq-mcp --sync to update.
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 |
MIT — See LICENSE
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"autoeq-mcp-server": {
"command": "npx",
"args": []
}
}
}