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jovezhong/mcp-timeplus

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Execute SQL queries and manage databases seamlessly with Timeplus. Leverage powerful tools to interact with your data, Kafka topics, and Iceberg tables efficiently. Enhance your da

J

MCP

jovezhong/mcp-timeplus

Added 1 June 2026

#clickhouse #flink #iceberg #kafka #mcp-server #spark

Overview

jovezhong/mcp-timeplus is a Model Context Protocol server that lets AI agents execute SQL queries and manage Timeplus databases. It provides tools for interacting with Timeplus data, Kafka topics, and Iceberg tables through a Python-based MCP interface.

Best for

Best for
Developers building AI agents that need to query Timeplus streaming databases

Use cases

  • Query Timeplus streaming data via AI agents
  • Manage Kafka topics through MCP
  • Access Iceberg tables from an LLM

How to use

Tools exposed

  • run_sql
  • list_databases
  • list_tables
  • list_kafka_topics
  • explore_kafka_topic
  • create_kafka_stream
  • connect_to_apache_iceberg

Tested with

Claude Desktop

Example client config

{\n  "mcpServers": {\n    "mcp-timeplus": {\n      "command": "uvx",\n      "args": ["mcp-timeplus"],\n      "env": {\n        "TIMEPLUS_HOST": "<timeplus-host>",\n        "TIMEPLUS_PORT": "<timeplus-port>",\n        "TIMEPLUS_USER": "<timeplus-user>",\n        "TIMEPLUS_PASSWORD": "<timeplus-password>",\n        "TIMEPLUS_SECURE": "false",\n        "TIMEPLUS_VERIFY": "true",\n        "TIMEPLUS_CONNECT_TIMEOUT": "30",\n        "TIMEPLUS_SEND_RECEIVE_TIMEOUT": "30",\n        "TIMEPLUS_READ_ONLY": "false",\n        "TIMEPLUS_KAFKA_CONFIG": "{\"bootstrap.servers\":\"a.aivencloud.com:28864\", \"sasl.mechanism\":\"SCRAM-SHA-256\",\"sasl.username\":\"avnadmin\", \"sasl.password\":\"thePassword\",\"security.protocol\":\"SASL_SSL\",\"enable.ssl.certificate.verification\":\"false\"}"\n      }\n    }\n  }\n}

Notes

jovezhong/mcp-timeplus is a Model Context Protocol server that lets AI agents execute SQL queries and manage Timeplus databases. It provides tools for interacting with Timeplus data, Kafka topics, and Iceberg tables through a Python-based MCP interface.

12 stars on GitHub. Last updated 2025-07-24. Licensed Apache-2.0.

Use cases

  • Query Timeplus streaming data via AI agents
  • Manage Kafka topics through MCP
  • Access Iceberg tables from an LLM

Pros

  • Exposes Timeplus database operations via standard MCP protocol
  • Enables natural language access to streaming SQL data
  • Lightweight Python implementation

Cons

  • Very small community (12 GitHub stars) indicates limited testing and support
  • Documentation may be sparse
  • Requires running an MCP host and a Timeplus instance

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Exposes Timeplus database operations via standard MCP protocol
  • Enables natural language access to streaming SQL data
  • Lightweight Python implementation

Cons

  • Very small community (12 GitHub stars) indicates limited testing and support
  • Documentation may be sparse
  • Requires running an MCP host and a Timeplus instance
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