Timeplus
Summary
This MCP server implementation provides a bridge to Timeplus, enabling AI assistants to execute SQL queries and retrieve database information. Developed by Jove Zhong, it offers tools for running select queries, listing databases, and listing tables within a specified database. The server is designed to work with Python 3.13 and utilizes the timeplus-connect library for API interactions. It focuses on read-only operations to ensure data safety and includes configuration options for easy integration with Claude Desktop. The implementation is particularly useful for AI applications requiring real-time analytics or data exploration capabilities within Timeplus environments.
Available Actions(7)
run_sql
Execute SQL queries on your Timeplus cluster. Input: sql (string): The SQL query to execute.
list_databases
List all databases on your Timeplus cluster.
list_tables
List all tables in a database. Input: database (string): The name of the database.
list_kafka_topics
List all topics in a Kafka cluster.
explore_kafka_topic
Show some messages in the Kafka topic. Input: topic (string): The name of the topic. message_count (int): The number of messages to show, default to 1.
create_kafka_stream
Setup a streaming ETL in Timeplus to save the Kafka messages locally. Input: topic (string): The name of the topic.
connect_to_apache_iceberg
Connect to a database based on Apache Iceberg. Input: iceberg_db (string): The name of the Iceberg database. aws_account_id (int): The AWS account ID (12 digits). s3_bucket (string): The S3 bucket name. aws_region (string): The AWS region, default to 'us-west-2'. is_s3_table_bucket (bool): Whether the S3 bucket is a S3 table bucket, default to False.
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