InfluxDB MCP Server

What does “InfluxDB” MCP Server do?

The InfluxDB MCP Server is a Model Context Protocol (MCP) server designed to provide seamless access to an InfluxDB instance using the InfluxDB OSS API v2. It acts as an intermediary tool that connects AI assistants to time-series data stored in InfluxDB, enabling enhanced workflows for developers and AI systems. Through its standardized interface, the server exposes both resources (such as organizations, buckets, and measurements) and tools (such as querying and writing data), empowering AI clients to perform tasks like executing database queries, managing data buckets, or integrating time-series analytics into their applications. This robust integration ensures that developers can automate data handling, streamline their development processes, and enhance their application’s intelligence by leveraging real-time and historical data from InfluxDB.

List of Prompts

• flux-query-examples: Provides common Flux query templates to simplify writing and executing typical InfluxDB queries.
• line-protocol-guide: Offers a guide and template for using InfluxDB’s line protocol format, aiding in data writing operations.

Ready to grow your business?

Start your free trial today and see results within days.

Request demo Log in

List of Resources

• Organizations List (influxdb://orgs): Displays all organizations present in the InfluxDB instance.
• Buckets List (influxdb://buckets): Shows all buckets with associated metadata.
• Bucket Measurements (influxdb://bucket/{bucketName}/measurements): Lists all measurements within a specified bucket.
• Query Data (influxdb://query/{orgName}/{fluxQuery}): Executes a Flux query and returns results as a resource.

List of Tools

• write-data: Write time-series data in InfluxDB’s line protocol format. Parameters include org, bucket, data, and optional precision.
• query-data: Executes Flux queries against the InfluxDB instance. Requires org and query parameters.
• create-bucket: Creates a new bucket in the database. Parameters: name, orgID, and optional retention period.
• create-org: Creates a new organization in InfluxDB. Parameters: name and optional description.

Join our newsletter

Get latest tips, trends, and deals for free.

Email address

Subscribe

Use Cases of this MCP Server

• Time-Series Data Querying: Easily run advanced Flux queries on InfluxDB data, empowering developers and AI agents to retrieve, analyze, and visualize time-series data.
• Automated Data Ingestion: Automate the process of writing data points to InfluxDB using the line protocol, streamlining IoT or telemetry pipelines.
• Database Management: Programmatically create new organizations and buckets, simplifying infrastructure management for large or multi-tenant InfluxDB deployments.
• Measurement Discovery: Dynamically list available measurements within a bucket, aiding applications that need to adapt to changing data schemas.
• AI-Assisted Analytics: Enable AI assistants to surface, contextualize, and manipulate InfluxDB data as part of broader analytic or monitoring workflows.

How to set it up

Windsurf

1. Ensure Node.js is installed on your machine.

2. Open the Windsurf configuration file (e.g., windsurf.json or equivalent).

3. Add the InfluxDB MCP Server to the mcpServers object:

   {
     "mcpServers": {
       "influxdb-mcp": {
         "command": "npx",
         "args": [
           "@idoru/influxdb-mcp-server@latest",
           "serve"
         ]
       }
     }
   }
   

4. Save the file and restart Windsurf.

5. Verify by checking that the InfluxDB MCP Server appears in the MCP server list.

Securing API Keys
Set sensitive values as environment variables. Example:

{
  "mcpServers": {
    "influxdb-mcp": {
      "command": "npx",
      "args": [
        "@idoru/influxdb-mcp-server@latest",
        "serve"
      ],
      "env": {
        "INFLUXDB_TOKEN": "${INFLUXDB_TOKEN_ENV}"
      }
    }
  }
}

Claude

1. Install Node.js if not already present.

2. Locate Claude’s configuration file.

3. Add the InfluxDB MCP Server to mcpServers:

   {
     "mcpServers": {
       "influxdb-mcp": {
         "command": "npx",
         "args": [
           "@idoru/influxdb-mcp-server@latest",
           "serve"
         ]
       }
     }
   }
   

4. Save changes and restart Claude.

5. Confirm setup via Claude’s interface.

Securing API Keys
(See Windsurf example above.)

Cursor

1. Ensure Node.js is present.

2. Open Cursor’s settings or configuration file.

3. Add the InfluxDB MCP Server using:

   {
     "mcpServers": {
       "influxdb-mcp": {
         "command": "npx",
         "args": [
           "@idoru/influxdb-mcp-server@latest",
           "serve"
         ]
       }
     }
   }
   

4. Save and restart Cursor.

5. Check the MCP server connectivity.

Securing API Keys
(See Windsurf example above.)

Cline

1. Make sure Node.js is installed.

2. Edit Cline’s configuration file.

3. Insert the following under mcpServers:

   {
     "mcpServers": {
       "influxdb-mcp": {
         "command": "npx",
         "args": [
           "@idoru/influxdb-mcp-server@latest",
           "serve"
         ]
       }
     }
   }
   

4. Save the file and restart Cline.

5. Validate that the server is active in Cline.

Securing API Keys
(See Windsurf example above.)

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "influxdb-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “influxdb-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Roots Support: ⛔ Not mentioned

Based on the above, this MCP server is well-documented for its core InfluxDB integration features. It clearly exposes resources and tools, includes prompt templates, and provides good setup guidance. However, advanced MCP features like roots and sampling are not documented, which slightly limits its extensibility for some workflows.

Our opinion

This is a robust, practical MCP server for InfluxDB with clear utility for time-series data and automation tasks. It scores high for practical developer use, though it lacks documentation on advanced MCP features.

MCP Score