Voice Leading Checker MCP. Instantly validate complex harmonic movement in your scores.
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Voice Leading Checker analyzes musical harmony by tracking how individual notes move between different chords. It evaluates four voices—Bass, Tenor, Alto, and Soprano—to flag errors like parallel fifths or voice crossing according to classical rules.
This tool helps composers instantly verify complex harmonic transitions, giving quality scores and suggested fixes for smooth, correct music.
What your AI agents can do
Analyze voice conduction
Checks the harmonic movement between two specific chord voicings.
Check voice hierarchy
Confirms that all four voices are correctly ordered in ascending pitch from bass to soprano.
Evaluate smoothness
Generates a quality grade for an entire musical transition based on its overall flow and error count.
It analyzes how the notes move when transitioning between two different chord voicings.
It validates that the four voices are presented in the required ascending order (Bass to Soprano).
It grades a musical transition based on how smooth and acoustically pleasing the overall movement is.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Voice Leading Checker: 3 Tools
These tools let you analyze the physical conduction between chords, verify structural voice order, and grade the overall smoothness of any musical transition.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Voice Leading Checker on Vinkius019ecb74analyze voice conduction
Checks the harmonic movement between two specific chord voicings.
019ecb74check voice hierarchy
Confirms that all four voices are correctly ordered in ascending pitch from bass to soprano.
019ecb74evaluate smoothness
Generates a quality grade for an entire musical transition based on its overall flow and error count.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Voice Leading Checker. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 3 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The Pain Point: Checking Harmony by Hand
Today, if a composer submits a score for review, the human expert has to open the sheet music, find every single chord change, and manually trace four separate lines of notes (Bass, Tenor, Alto, Soprano). They then have to cross-reference those movements against decades of musical theory—checking for parallel fifths or octave errors, all while keeping track of pitch levels. It's a massive time sink.
With this MCP, you skip the manual tracing entirely. You input your score data, and the agent handles the entire audit. You get back an immediate breakdown of every structural flaw. The result isn't just 'it failed'; it tells you exactly which rule was broken and how to fix it.
Voice Leading Checker: What You Get
You stop wasting time on subjective reviews. Instead, the system provides objective data points for conduction analysis, confirming if notes move correctly between chord voicings. It also runs a mandatory check to validate that every single voice maintains its required ascending pitch order.
The difference is quantifiable certainty. You move from guessing why something sounds wrong to receiving an actionable report with quality scores and suggested corrections.
What you can do with this MCP connector
You're building a piece of music, but the harmony feels off. You can't quite pinpoint why it sounds dissonant, even if the chords themselves are right. This MCP examines the movement of every note across four distinct melodic lines (SATB). It checks for harmonic mistakes that human ears might miss or take forever to verify—things like parallel fifths, voice crossings, and incorrect structural flow.
Instead of manually poring over sheet music and cross-referencing theory books, you send your voicings to the system. The service returns specific violation details, a quality score for the transition, and concrete suggestions on how to fix it. Because this kind of detailed analysis requires absolute trust in the data, every tool call is recorded in a cryptographically signed audit trail.
That means you always know exactly what the AI agent checked and why. You connect once from your preferred client—Claude, Cursor, Windsurf, or any MCP-compatible client—and suddenly complex composition rules are actionable.
019ecb75-06e2-701f-bba3-9c5ab5a8fae5 
How Voice Leading Checker MCP Works
- 1 You feed the MCP two sets of chord voicings, specifying which notes are in the Bass, Tenor, Alto, and Soprano registers.
- 2 The system runs three checks: first for structural hierarchy, then for conduction between the chords, and finally grades the overall smoothness score.
- 3 You receive a detailed report listing all detected harmonic errors (like parallel fifths) along with suggestions to correct them.
The bottom line is you get an immediate, expert critique of your composition's harmony, pinpointing exactly where and why the music fails its classical rules.
Who Is Voice Leading Checker MCP For?
This MCP is for composers, musicologists, and audio engineers who rely on complex harmonic structures. If you spend time manually checking musical theory rules or struggling to verify score integrity, this tool saves hours of painstaking work.
Uses the MCP to validate a new piece's transitions, ensuring that every chord movement maintains classical harmonic purity before recording.
Tests complex theoretical concepts by inputting example voicings and using the system to prove or disprove adherence to specific musical rules.
Verifies that transcribed scores are playable and harmonically sound, especially when arranging music across different voice types (SATB).
What Changes When You Connect
- Pinpoint exact errors: Use
analyze_voice_conductionto detect specific issues, like forbidden parallel fifths or octaves between chord changes. You don't just get a 'fail'; you get the rule broken and where. - Verify structural flow: Run
check_voice_hierarchyfirst. This ensures your foundational voices are always in proper ascending order, fixing basic arrangements that would otherwise sound amateur. - Get a quality grade instantly: Instead of subjective listening tests,
evaluate_smoothnessgives you an objective score based on error count and leap magnitude, helping you gauge the overall polish of the transition. - Build complex routines: Chain this MCP with other tools in the Vinkius catalog to build automated checks that validate entire musical movements across multiple platforms.
- Trust the results: Every analysis is captured in a tamper-proof audit trail. You always know exactly what data was used and how the AI arrived at its score.
Real-World Use Cases
A composer needs to check a complex jazz harmony.
A user inputs a transition from a dense, initial voicing to a simpler target voicing. The agent uses analyze_voice_conduction and reports that while the chords are valid, the movement creates excessive melodic leaps and parallel fifths, requiring specific note adjustments in the Tenor and Alto lines.
An educator is grading student scores.
The agent runs check_voice_hierarchy on several submitted voicings. It immediately flags multiple instances where a voice (like the Tenor) dips below the Bass line, proving structural failure and allowing the teacher to grade accurately without manual checking.
An arranger needs to make a passage sound 'better'.
The user runs evaluate_smoothness on a section that sounds choppy. The tool returns a low quality score, pointing out the exact moments of high error count and suggesting specific note shifts needed to achieve a smoother acoustic polish.
A film scoring team needs rapid compliance checks.
The agent takes several candidate transitions from different sections of the score. It runs analyze_voice_conduction repeatedly, providing a master list of all harmonic violations across 20 minutes of music in minutes.
The Tradeoffs
General AI text prompts
Asking an LLM like 'Is this music good?' or 'Fix the harmony here' because you can't find a specific tool.
→
Don't ask for subjective feelings. Use analyze_voice_conduction to check transitions, then use check_voice_hierarchy if structure is questioned, and finally run evaluate_smoothness for an objective quality grade.
Ignoring voice order
Assuming the notes are fine because they sound okay in isolation. This ignores fundamental structural rules of harmony.
→
Always start with check_voice_hierarchy. It forces validation that voices follow the correct ascending pitch structure before you analyze their movement.
Only checking chords, not movement
Checking two separate voicings and thinking they're fine. This misses the transition rules entirely.
→
You must run analyze_voice_conduction to compare the initial voicing against the target voicing. That’s what reveals parallel fifths.
When It Fits, When It Doesn't
Use this MCP if your goal is structural verification: you need proof that a piece of music follows specific, measurable rules (like voice order or harmonic movement). If you only care about whether the chords sound 'pretty good,' this tool isn't needed. Instead, use simple chord recognition tools for basic naming. But if you are composing and need to ensure your work is classically compliant—if you need to know why a transition fails based on physical rules like parallel fifths or voice crossing—this MCP is required. It gives you the objective metrics that general AI models can't provide.
Common Questions About Voice Leading Checker MCP
How does the analyze_voice_conduction tool work? +
It compares two specific chord voicings, determining how the notes move from one set of chords to the next. It detects harmonic violations like parallel fifths or octaves that ruin smooth musical transitions.
Do I need to use check_voice_hierarchy first? +
Yes, it’s best practice. Running check_voice_hierarchy confirms the foundational structure—that the voices are in proper ascending order (B-T-A-S). This step prevents misleading analysis of movement if the basic structure is already flawed.
What does evaluate_smoothness actually measure? +
evaluate_smoothness doesn't just count errors; it generates a composite quality grade. This score considers both the number of violations and the magnitude of melodic leaps, giving you a holistic view of the transition’s polish.
Can I use this MCP with other music tools? +
Absolutely. You can chain it with other specialized MCPs in the Vinkius catalog to build full automated workflows that test harmony, structure, and flow across multiple musical parameters at once.
What input format does the analyze_voice_conduction tool require for pitches? +
You must provide pitches using their numerical MIDI values. The tool requires four specific inputs: Bass, Tenor, Alto, and Soprano. This ensures precise analysis of the harmonic movement.
If my voicing is invalid, how does check_voice_hierarchy report the error? +
The tool immediately rejects the data and specifies exactly which pitch violates the ascending order rule. For instance, it will tell you if the Tenor is lower than the Bass.
Are there rate limits when using evaluate_smoothness for large musical passages? +
No. Vinkius manages all background processing load on this MCP. You pay per call, and we handle the infrastructure capacity so you don't hit usage caps.
Does the engine detect errors other than parallel fifths or octaves? +
Yes. In addition to those classic rules, it flags voice crossing instances and assesses excessively large melodic leaps. The resulting quality score helps gauge overall harmonic tension.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
