AutoEQ MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AutoEQ MCP Server Compare the Sennheiser HD 600 and the HiFiMAN Sundara"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AutoEQ MCP Server
pip install autoeq-mcpAn 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.
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

Finding similar IEMs by sound signature

Tools
Tool | Description |
| Search by name, type (over-ear/in-ear/earbud), sound signature, or measurement source |
| Get full EQ profile — parametric EQ, fixed band EQ, per-band analysis with visual bars |
| Side-by-side comparison of two headphones across all frequency bands |
| Recommendations by preference (neutral, warm, bright, bass, vocal, fun, analytical) |
| Harman headphone listener preference score rankings |
| List all 61 available target curves (Harman, Diffuse Field, etc.) |
| 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-mcpFor 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 --sseFrom Source
git clone https://github.com/verIdyia/autoeq-mcp
cd autoeq-mcp
pip install -e .
autoeq-mcp --syncConfiguration
All configuration is via environment variables:
Variable | Default | Description |
|
| Directory for repo clone and SQLite DB |
|
| SSE server port |
|
| SSE server host |
| (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 toolseq_compareARead-onlyIdempotent
Compare two headphones band-by-band with sound signature analysis.
Inputs: name1 (required), name2 (required). Returns: result
eq_profileBRead-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_rankingARead-onlyIdempotent
Get headphone rankings by Harman headphone listener preference score.
Inputs: form_factor, limit. Returns: result
eq_recommendARead-onlyIdempotent
Recommend headphones based on sound preference and type. Sorted by Harman preference score.
Inputs: preference, form_factor, limit. Returns: result
eq_searchARead-onlyIdempotent
Search the AutoEQ database for headphones/IEMs. Filter by name, type, sound signature, or measurement source.
Inputs: query, form_factor, signature, source, +1 more. Returns: result
eq_syncAIdempotent
Pull latest AutoEQ data from GitHub and rebuild the database. May take a few minutes.
Inputs: no named parameters. Returns: result
eq_targetsARead-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
Scored across 7 tools
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.
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.
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.
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.
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