MindsDB Supercharges Google's MCP Toolbox with Unstructured Data Support

We’re happy to announce that we’ve integrated MindsDB with Google's open-source project, MCP (Model Context Protocol) Toolbox. This will make your AI applications very, very smart. This enhancement expands the Toolbox's reach, especially for organizations grappling with lots of siloed data.

The MindsDB integration at a glance

At its core, MindsDB is a federated query engine designed specifically for AI applications, that acts as a universal translator, enabling you to query hundreds of data sources (structured, semi-structured, unstructured) using familiar SQL. We’ve contributed this powerful capability as a new connector into MCP Toolbox, allowing developers and AI agents to seamlessly interact with a broader spectrum of enterprise data through MindsDB.

Now, with MindsDB, MCP Toolbox can connect to hundreds of datasources, including popular business applications like Salesforce, Jira, and GitHub, and even unstructured data sources like Gmail and Slack. This means you can break down data silos and connect all your data from a single API to your AI applications. Popular use cases include AI-powered search, analytics, and the ability to provide real-time data to agentic applications.

Key features: Bridging structured and unstructured worlds

MindsDB brings several essential features to the MCP Toolbox:

• Datasource expansion: The most immediate and impactful benefit is the sheer volume of new data sources accessible. Imagine querying Salesforce opportunities alongside GitHub activity, or analyzing email patterns with Slack conversations—all through a unified interface. This greatly expands the Toolbox's utility for enterprise users.

• SQL interface for any data: This is where MindsDB shines for developers. It allows you to write standard SQL queries that automatically translate to various API protocols, including REST APIs, GraphQL, and native protocols. This reduces the complexity and learning curve associated with accessing diverse data.

• Cross-datasource AI analytics: MindsDB SQL capability makes it possible to perform joins and analytics across different data sources. For instance, you can correlate sales data from Salesforce with development activity from GitHub, providing a holistic view of your business operations that was previously unattainable. To facilitate this, MindsDB treats each data source, unstructured or structured, as a virtual table, facilitating sophisticated SQL operations across all sources.

• Access to unstructured data: MindsDB provides over 200 data connectors with knowledge bases bringing the ability of indexing text data that then can be queried using SQL.

• Knowledge Bases for unstructured data: MindsDB allows you to create Knowledge Bases which are essentially autonomous Retrieval-Augmented Generation (RAG) systems. You can ingest unstructured data like emails (Gmail/Outlook), messages (Slack, Microsoft Teams, Discord), and files (S3, filesystems) into these knowledge bases. Once ingested, this unstructured data becomes queryable by an AI application or model. This querying across data sources is facilitated by an auto-generated data catalog that contains metadata and a relational model across all data sources.  MindsDB also supports hybrid search, combining vector similarity with keyword search to surface the most relevant results for AI search and analytics use cases.

Technical implementation updates

This integration brings a suite of powerful updates and benefits for developers:

• New MindsDB Source implementation: The Toolbox now includes a new MindsDB source implementation, leveraging the MySQL wire protocol for robust connectivity.

• Comprehensive test coverage: Extensive unit and integration tests ensure reliability and backward compatibility of the new MindsDB tools with existing SQL features.

• Dedicated MindsDB tools: New mindsdb-execute-sql for direct SQL execution and mindsdb-sql for parameterized queries offer enhanced flexibility.

Quickstart Guide

To get started with MindsDB and the MCP Toolbox, follow these general steps:

1. Set up the MCP Toolbox: Ensure you have the MCP Toolbox service running. You can find detailed instructions in the official documentation.

2. Install MindsDB: The fastest way to get MindsDB up and running is via Docker:

docker run --name mindsdb_container \
-e MINDSDB_APIS=http,mysql \
-p 47334:47334 -p 47335:47335 \
mindsdb/mindsdb

For more installation options (e.g., PyPI), refer to the MindsDB documentation.

3. Connect your data sources in MindsDB: Within MindsDB, you'll need to create "databases" that connect to your external data sources (e.g., Salesforce, GitHub, Gmail). This typically involves CREATE DATABASE statements. For example:

-- connect to salesforce
CREATE DATABASE salesforce_datasource
WITH
    ENGINE = 'salesforce',
    PARAMETERS = {
        "username": "your-username@email.com",
        "password": "your-password",
        "client_id": "your-client-id",
        "client_secret": "your-client-secret"
    };

-- connect to postgres
CREATE DATABASE postgresql_datasource 
WITH
ENGINE = 'postgres', 
PARAMETERS = {
    "host": "postgres.sample.com",
    "port": 5432,
    "database": "postgres",
    "user": "postgres",
    "schema": "data",
    "password": "password"
};

Refer to the MindsDB documentation for specific connector details.

4. Use MindsDB Tools in MCP Toolbox: The MindsDB integration within MCP Toolbox allows you to execute SQL queries across your connected MindsDB data sources. You can use tools like mindsdb-execute-sql for direct querying.

-- run federated queries
SELECT *
FROM salesforce_datasource.account a
JOIN postgresql_datasource.customer_int c
ON a.`Id` = c.`AccountId`;

5. Unify data into the Knowledge Base: You can then load data into a MindsDB ‘Knowledge Base’, which is particularly useful for things like semantic search over large, unstructured data sets.

CREATE KNOWLEDGE_BASE my_kb
USING
    embedding_model = {
        "provider": "openai",
        "model_name" : "text-embedding-3-large",
        "api_key": "sk-..."
    },
    reranking_model = {
        "provider": "openai",
        "model_name": "gpt-4o",
        "api_key": "sk-..."
    },
    storage = my_vector_store.storage_table,
    metadata_columns = ['AccountId', 'Created_At', ...],
    content_columns = ['Description', 'Notes', ...],
    id_column = 'Id';


INSERT INTO my_kb
	SELECT *
	FROM salesforce_datasource.account a
	JOIN postgresql_datasource.customer_int c
	ON a.`Id` = c.`AccountId`;

Learn more about knowledge bases here.

Resources

• MCP Toolbox GitHub repo: https://github.com/googleapis/genai-toolbox 

• MindsDB in MCP Toolbox: https://github.com/googleapis/genai-toolbox/blob/main/docs/en/resources/tools/mindsdb/_index.md 

• MindsDB Documentation: https://docs.mindsdb.com/

• MindsDB GitHub:https://github.com/mindsdb/mindsdb