How to Use the Chord Progression Analyzer MCP in Google ADK

Q: If I use Chord Progression Analyzer with the Google ADK, how is the emotional context integrated?

The lookupmusicalcontext tool pulls genre and mood metadata. This enriches your analysis, letting you connect a specific harmonic pattern (like minor chords) to an expected emotion in your application.

Connect music theory analysis with BigQuery data using the Google ADK.

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Connect Chord Progression Analyzer MCP to Google ADK

Create your Vinkius account to connect Chord Progression Analyzer to Google ADK 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 Progression Analyzer with Google ADK

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Convert chords to functional Roman numerals

The `analyze_roman_numerals` tool converts a list of named chords into their corresponding Roman numeral functions, relative to a specified key. This capability is essential for grounding musical data within your existing BigQuery datasets. It ensures that whether the user inputs Cmaj7 or its enharmonic equivalent, the output always relates correctly to the established scale degrees.

Classify complex harmonic progressions

Using `classify_progression`, you analyze a sequence of Roman numerals to pinpoint the core harmonic pattern. The result goes beyond simple naming; it reveals if the movement is resolving or creating sustained tension. This allows your Google ADK agents to categorize music theory components and join them with other large-scale data sets.

Look up genre-specific musical context

The `lookup_musical_context` tool retrieves style and emotional metadata attached to a progression type. You can correlate this information against user behavior or market trends in your Google Cloud infrastructure. This capability gives depth, letting you understand the 'why' behind a certain chord sequence.

Setup guide

Set up Chord Progression Analyzer MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Chord Progression Analyzer tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Chord Progression Analyzer_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Chord Progression Analyzer tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Chord Progression Analyzer MCP in Google ADK

You pass the MCP toolset to your LlmAgent, allowing it to execute musical analysis directly. The resulting structured data can then be fed into BigQuery for large-scale enterprise reasoning.

It identifies foundational functional relationships by examining the sequence of Roman numerals, determining if the progression follows predictable Western harmony or something more complex. It's built for structural analysis.

Yes. While focused on music theory, its output of generalized harmonic patterns makes it useful for any domain that requires structured, theoretical data mapping.

The `lookup_musical_context` tool pulls genre and mood metadata. This enriches your analysis, letting you connect a specific harmonic pattern (like minor chords) to an expected emotion in your application.

It touches raw chord sequences and outputs classified harmonic patterns. This ensures that the theoretical output is clean, structured, and ready for ingestion into your Google Cloud workflows.

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