MCP Toolbox for Databases

--- title: "mindsdb-execute-sql" type: docs weight: 1 description: > A "mindsdb-execute-sql" tool executes a SQL statement against a MindsDB federated database. aliases: - /resources/tools/mindsdb-execute-sql --- ## About A `mindsdb-execute-sql` tool executes a SQL statement against a MindsDB federated database. It's compatible with any of the following sources: - [mindsdb](../../sources/mindsdb.md) `mindsdb-execute-sql` takes one input parameter `sql` and runs the SQL statement against the `source`. This tool enables you to: - **Query Multiple Datasources**: Execute SQL across hundreds of connected datasources - **Cross-Datasource Joins**: Perform joins between different databases, APIs, and file systems - **ML Model Predictions**: Query ML models as virtual tables for real-time predictions - **Unstructured Data**: Query documents, images, and other unstructured data as structured tables - **Federated Analytics**: Perform analytics across multiple datasources simultaneously - **API Translation**: Automatically translate SQL queries into REST APIs, GraphQL, and native protocols ## Example Queries ### Cross-Datasource Analytics ```sql -- Join Salesforce opportunities with GitHub activity SELECT s.opportunity_name, s.amount, g.repository_name, COUNT(g.commits) as commit_count FROM salesforce.opportunities s JOIN github.repositories g ON s.account_id = g.owner_id WHERE s.stage = 'Closed Won' GROUP BY s.opportunity_name, s.amount, g.repository_name; ``` ### Email & Communication Analysis ```sql -- Analyze email patterns with Slack activity SELECT e.sender, e.subject, s.channel_name, COUNT(s.messages) as message_count FROM gmail.emails e JOIN slack.messages s ON e.sender = s.user_name WHERE e.date >= '2024-01-01' GROUP BY e.sender, e.subject, s.channel_name; ``` ### ML Model Predictions ```sql -- Use ML model to predict customer churn SELECT customer_id, customer_name, predicted_churn_probability, recommended_action FROM customer_churn_model WHERE predicted_churn_probability > 0.8; ``` ### MongoDB Query ```sql -- Query MongoDB collections as structured tables SELECT name, email, department, created_at FROM mongodb.users WHERE department = 'Engineering' ORDER BY created_at DESC; ``` > **Note:** This tool is intended for developer assistant workflows with > human-in-the-loop and shouldn't be used for production agents. ## Example ```yaml tools: execute_sql_tool: kind: mindsdb-execute-sql source: my-mindsdb-instance description: Use this tool to execute SQL statements across multiple datasources and ML models. ``` ### Working Configuration Example Here's a working configuration that has been tested: ```yaml sources: my-pg-source: kind: mindsdb host: 127.0.0.1 port: 47335 database: files user: mindsdb tools: mindsdb-execute-sql: kind: mindsdb-execute-sql source: my-pg-source description: | Execute SQL queries directly on MindsDB database. Use this tool to run any SQL statement against your MindsDB instance. Example: SELECT * FROM my_table LIMIT 10 ``` ## Reference | **field** | **type** | **required** | **description** | |-------------|:--------:|:------------:|----------------------------------------------------| | kind | string | true | Must be "mindsdb-execute-sql". | | source | string | true | Name of the source the SQL should execute on. | | description | string | true | Description of the tool that is passed to the LLM. |