How to Use the Chord Constructor MCP in LlamaIndex

Index musical knowledge with the Chord Constructor MCP Server for LlamaIndex.

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Connect Chord Constructor MCP to LlamaIndex

Create your Vinkius account to connect Chord Constructor to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Chord Constructor with LlamaIndex

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Perplexity

Decompose Chords using LlamaIndex

Start by calling `parse_chord_string` to convert messy chord notation into clean, structured parts. This gives your knowledge index the root, intervals, and a normalized name for every input. This parsed data is what you want to search against later; it's far more useful than just storing text.

Find Chord Inversions with LlamaIndex

If the user queries about how a chord should sound in different bass registers, the index can use `get_chord_inversions`. This tool calculates all possible note permutations that serve as the bass while maintaining harmonic integrity. This means your RAG application doesn't just retrieve documents; it retrieves mathematically verified musical data.

Analyze Function using LlamaIndex

LlamaIndex can index the results of `determine_harmonic_role`. When a user asks, 'What is the function of this chord in a minor key?' your system pulls from the indexed data to give an accurate, grounded answer. This turns a simple API call into verifiable knowledge within your searchable index.

Setup guide

Set up Chord Constructor MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Chord Constructor MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Chord Constructor tools.",
)
response = await agent.run("List recent Chord Constructor data")

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Common questions about Chord Constructor MCP in LlamaIndex

By indexing the structured output of this MCP, you can answer highly specific musical questions. Instead of searching text, you're querying verified harmonic relationships.

Yes. You combine document chunks with the live data from this MCP. Your index becomes a mix of academic papers and real-time harmonic analysis.

It handles musical notation strings (e.g., 'Cmaj7/G') which are analyzed into structured note components, intervals, and harmonic roles.

The tools provide deterministic outputs based on established rules of harmony, meaning the data you index is mathematically consistent and traceable.

If your application needs to ground its answers in real-world musical theory or structural analysis, then yes, this MCP is essential for extending the knowledge base.

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