Skip to main content
Glama

mcp_opendaw_analyze_stereo

Read-only

Analyze the stereo field of a WAV file to measure width, L/R balance, phase correlation, and mono compatibility. Get per-region width and mix suggestions for balancing or widening issues.

Instructions

Stereo analysis of audio — width, L/R balance, mono compatibility, mid/side energy.

Analyzes the stereo field of a track:

  • stereo_width: Side/Mid RMS ratio (0 = mono, 0.5+ = wide, 1.0 = hard panned)

  • lr_balance: L/R energy difference (-1 = fully left, 0 = centered, +1 = fully right)

  • phase_correlation: -1 to +1 (+1 = mono safe, 0 = uncorrelated, -1 = out of phase)

  • mono_compatible: True if phase correlation > 0 (collapses to mono without cancellation)

  • phase_issues_pct: % of samples where L and R have opposite polarity

  • Per-region width: low (<250Hz), mid (250-4000Hz), high (4000+Hz) Helps identify if stereo width is well-distributed or concentrated in one region

Mix decision guidance:

  • stereo_width < 0.1 → narrow/mono mix, consider widening

  • stereo_width > 0.8 → very wide, check mono compatibility

  • phase_correlation < 0 → phase issues, will cancel in mono

  • lr_balance > 0.2 → right-heavy, consider rebalancing

  • lr_balance < -0.2 → left-heavy, consider rebalancing

  • Low-freq width > 0.3 → bass is wide (usually undesirable, keep bass mono)

Args: filename: Name of the WAV file in the exports directory (without path), or absolute path to any WAV file.

Returns stereo descriptors, per-region width, and mix suggestions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Annotations provide readOnlyHint=true, and the description adds behavioral context by explaining it reads a WAV file and computes stereo metrics. It also clarifies filename semantics (exports directory or absolute path) and gives detailed metric definitions, going beyond the minimal read-only annotation.

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?

The description is long but well-structured with a summary header, detailed metric bullet points, mix decision guidance, and an Args section. The information density is high and each section serves a purpose, though it could be slightly trimmed without losing value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers input (filename), output (stereo descriptors, per-region width, mix suggestions), and interpretation (decision thresholds). Even though an output schema exists, the description goes beyond by explaining metric ranges and actionable guidance, making it complete for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only provides the parameter title with no description (0% coverage), but the description's Args section fully explains the filename parameter: 'Name of the WAV file in the exports directory (without path), or absolute path to any WAV file.' This fully compensates for the lack of schema detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Stereo analysis of audio' and lists specific outputs like stereo_width, lr_balance, and phase_correlation. It distinguishes itself from sibling tools such as analyze_mix or analyze_dynamics by focusing on stereo-specific metrics and per-region width.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Mix decision guidance' section provides explicit thresholds (e.g., stereo_width < 0.1 means narrow) that indicate when to act on results. However, it does not explicitly mention when not to use this tool or how it compares to other analysis tools, so it lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ameobius-ai/opendaw-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server