# How to integrate Influxdb cloud MCP with Autogen ```json { "title": "How to integrate Influxdb cloud MCP with Autogen", "toolkit": "Influxdb cloud", "toolkit_slug": "influxdb_cloud", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/influxdb_cloud/framework/autogen", "markdown_url": "https://composio.dev/toolkits/influxdb_cloud/framework/autogen.md", "updated_at": "2026-05-12T10:15:53.118Z" } ``` ## Introduction This guide walks you through connecting Influxdb cloud to AutoGen using the Composio tool router. By the end, you'll have a working Influxdb cloud agent that can write temperature sensor data to bucket, add cpu usage graph to dashboard, update retention policy for analytics data through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Influxdb cloud account through Composio's Influxdb cloud MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Influxdb cloud with - [OpenAI Agents SDK](https://composio.dev/toolkits/influxdb_cloud/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/influxdb_cloud/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/influxdb_cloud/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/influxdb_cloud/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/influxdb_cloud/framework/codex) - [OpenClaw](https://composio.dev/toolkits/influxdb_cloud/framework/openclaw) - [Hermes](https://composio.dev/toolkits/influxdb_cloud/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/influxdb_cloud/framework/cli) - [Google ADK](https://composio.dev/toolkits/influxdb_cloud/framework/google-adk) - [LangChain](https://composio.dev/toolkits/influxdb_cloud/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/influxdb_cloud/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/influxdb_cloud/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/influxdb_cloud/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/influxdb_cloud/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Install the required dependencies for Autogen and Composio - Initialize Composio and create a Tool Router session for Influxdb cloud - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Influxdb cloud tools - Run a live chat loop where you ask the agent to perform Influxdb cloud operations ## What is AutoGen? Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows. Key features include: - Multi-Agent Systems: Build collaborative agent workflows - MCP Workbench: Native support for Model Context Protocol tools - Streaming HTTP: Connect to external services through streamable HTTP - AssistantAgent: Pre-built agent class for tool-using assistants ## What is the Influxdb cloud MCP server, and what's possible with it? The Influxdb cloud MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your InfluxDB Cloud account. It provides structured and secure access to your time series data, letting your agent run queries, ingest new data, manage dashboards, and update user settings automatically. - Real-time data ingestion and writing: Instantly send line protocol data points to your InfluxDB Cloud buckets for seamless time series collection and analytics. - Automated dashboard cell management: Direct your agent to add new cells to existing dashboards, making it easy to visualize and monitor the latest metrics or results. - Advanced query analysis and validation: Have the agent generate and inspect Flux query Abstract Syntax Trees (AST) to validate and debug your analytics scripts before running them. - User and session management: Enable your agent to sign users in or out and even delete users by ID, supporting secure and automated access control. - DBRP mapping updates and retrieval: Let your agent fetch or update Database Retention Policy (DBRP) mappings, so you can adapt your data retention and default policies on the fly. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `INFLUXDB_CLOUD_ADD_DASHBOARD_CELL` | Add Dashboard Cell | Tool to add a cell to a dashboard. Use when you want to add or copy a cell to an existing dashboard after verifying the dashboard exists. | | `INFLUXDB_CLOUD_DELETE_USER` | Delete User | Delete a user from InfluxDB Cloud by their user ID. This action permanently removes a user from the InfluxDB Cloud organization. Requires an operator token with write:users permission to execute successfully. Use this when you need to remove a user's access to the InfluxDB Cloud organization. | | `INFLUXDB_CLOUD_GENERATE_QUERY_AST` | Generate Flux Query AST | Generates an Abstract Syntax Tree (AST) from a Flux query script. Use this tool to analyze the structure of a Flux query and validate its syntax. The AST shows the parsed structure but does not validate semantic correctness (e.g., whether buckets or fields exist). | | `INFLUXDB_CLOUD_GET_DBRP` | Get DBRP Mapping | Retrieve a Database and Retention Policy (DBRP) mapping by ID from InfluxDB Cloud. DBRP mappings enable InfluxDB 1.x query compatibility by mapping old database/retention policy names to InfluxDB 2.x buckets. Use this action to: - Verify which bucket a 1.x database/retention policy maps to - Check if a mapping is the default for its database - Inspect mapping configuration before updating or querying data with 1.x APIs Prerequisites: You must have a valid DBRP mapping ID (obtain via listing DBRP mappings or from previous create operations). | | `INFLUXDB_CLOUD_LIST_ROUTES` | List Routes | Lists all available InfluxDB v2 API endpoints and routes. This action queries the root API endpoint (GET /api/v2) to retrieve a comprehensive map of all available API resources and their corresponding URLs. Use this to discover what endpoints are available in your InfluxDB Cloud instance, including resources for data management (buckets, write, delete, query), user management (users, orgs, authorizations), monitoring (checks, tasks, dashboards), and configuration (labels, variables, Telegraf). The response includes both simple route strings (e.g., "/api/v2/buckets") and nested route objects (e.g., query routes with analyze, ast, suggestions endpoints). Authentication: Requires a valid authorization token in the metadata headers. | | `INFLUXDB_CLOUD_SIGNIN` | Sign In | Authenticates a user with username and password to create a session with InfluxDB Cloud. Returns a session cookie that can be used for subsequent API requests instead of token-based authentication. Use this when you need to authenticate with user credentials rather than API tokens, or when establishing a user session for operations that require session-based authentication. | | `INFLUXDB_CLOUD_SIGNOUT` | Sign Out | Tool to expire a user session using a session cookie. Use when ending an authenticated session after signin. | | `INFLUXDB_CLOUD_UPDATE_DBRP` | Update DBRP | Tool to update a DBRP mapping's default and retention policy. Use when modifying an existing DBRP mapping after initial creation. | | `INFLUXDB_CLOUD_WRITE_DATA` | Write Line Protocol Data | Writes time-series data in line protocol format to an InfluxDB Cloud bucket. Use this tool to ingest metrics, sensor data, or any time-series measurements into InfluxDB. The data must be formatted according to InfluxDB line protocol specification. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Influxdb cloud MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Influxdb cloud. Instead of manually wiring Influxdb cloud APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Influxdb cloud account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) and create an API key. You'll need credits to use the models, or you can connect to another model provider. - Keep the API key safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Influxdb cloud via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables Create a .env file in your project folder. What's happening: - COMPOSIO_API_KEY is required to talk to Composio - OPENAI_API_KEY is used by Autogen's OpenAI client - USER_ID is how Composio identifies which user's Influxdb cloud connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Influxdb cloud tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Influxdb cloud session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["influxdb_cloud"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for. What's happening: - url points to the Tool Router MCP endpoint from Composio - timeout is the HTTP timeout for requests - sse_read_timeout controls how long to wait when streaming responses - terminate_on_close=True cleans up the MCP server process when the workbench is closed ```python # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Influxdb cloud tools from the workbench ```python # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Influxdb cloud assistant agent with MCP tools agent = AssistantAgent( name="influxdb_cloud_assistant", description="An AI assistant that helps with Influxdb cloud operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Influxdb cloud tools to call via MCP - agent.run_stream(...) yields streaming messages as the agent thinks and calls tools - Typing exit, quit, or bye ends the loop ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Influxdb cloud related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ["exit", "quit", "bye"]: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, "content") and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Influxdb cloud session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["influxdb_cloud"] ) url = session.mcp.url # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Influxdb cloud assistant agent with MCP tools agent = AssistantAgent( name="influxdb_cloud_assistant", description="An AI assistant that helps with Influxdb cloud operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Influxdb cloud related question or task to the agent.\n") # Conversation loop while True: user_input = input("You: ").strip() if user_input.lower() in ['exit', 'quit', 'bye']: print("\nGoodbye!") break if not user_input: continue print("\nAgent is thinking...\n") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, 'content') and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You now have an Autogen assistant wired into Influxdb cloud through Composio's Tool Router and MCP. From here you can: - Add more toolkits to the toolkits list, for example notion or hubspot - Refine the agent description to point it at specific workflows - Wrap this script behind a UI, Slack bot, or internal tool Once the pattern is clear for Influxdb cloud, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Influxdb cloud MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/influxdb_cloud/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/influxdb_cloud/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/influxdb_cloud/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/influxdb_cloud/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/influxdb_cloud/framework/codex) - [OpenClaw](https://composio.dev/toolkits/influxdb_cloud/framework/openclaw) - [Hermes](https://composio.dev/toolkits/influxdb_cloud/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/influxdb_cloud/framework/cli) - [Google ADK](https://composio.dev/toolkits/influxdb_cloud/framework/google-adk) - [LangChain](https://composio.dev/toolkits/influxdb_cloud/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/influxdb_cloud/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/influxdb_cloud/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/influxdb_cloud/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/influxdb_cloud/framework/crew-ai) ## Related Toolkits - [Excel](https://composio.dev/toolkits/excel) - Microsoft Excel is a robust spreadsheet application for organizing, analyzing, and visualizing data. 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It helps organizations meet GDPR, CCPA, and HIPAA data privacy compliance requirements. - [Api ninjas](https://composio.dev/toolkits/api_ninjas) - Api ninjas offers 120+ public APIs spanning categories like weather, finance, sports, and more. Developers use it to supercharge apps with real-time data and actionable endpoints. - [Api sports](https://composio.dev/toolkits/api_sports) - Api sports is a comprehensive sports data platform covering 2,000+ competitions with live scores and 15+ years of stats. Instantly access up-to-date sports information for analysis, apps, or chatbots. - [Apify](https://composio.dev/toolkits/apify) - Apify is a cloud platform for building, deploying, and managing web scraping and automation tools called Actors. It lets you automate data extraction and workflow tasks at scale—no infrastructure headaches. - [Autom](https://composio.dev/toolkits/autom) - Autom is a lightning-fast search engine results data platform for Google, Bing, and Brave. Developers use it to access fresh, low-latency SERP data on demand. - [Beaconchain](https://composio.dev/toolkits/beaconchain) - Beaconchain is a real-time analytics platform for Ethereum 2.0's Beacon Chain. It provides detailed insights into validators, blocks, and overall network performance. - [Big data cloud](https://composio.dev/toolkits/big_data_cloud) - BigDataCloud provides APIs for geolocation, reverse geocoding, and address validation. Instantly access reliable location intelligence to enhance your applications and workflows. - [Bigpicture io](https://composio.dev/toolkits/bigpicture_io) - BigPicture.io offers APIs for accessing detailed company and profile data. Instantly enrich your applications with up-to-date insights on 20M+ businesses. - [Bitquery](https://composio.dev/toolkits/bitquery) - Bitquery is a blockchain data platform offering indexed, real-time, and historical data from 40+ blockchains via GraphQL APIs. Get unified, reliable access to complex on-chain data for analytics, trading, and research. - [Brightdata](https://composio.dev/toolkits/brightdata) - Brightdata is a leading web data platform offering advanced scraping, SERP APIs, and anti-bot tools. It lets you collect public web data at scale, bypassing blocks and friction. - [Builtwith](https://composio.dev/toolkits/builtwith) - BuiltWith is a web technology profiler that uncovers the technologies powering any website. Gain actionable insights into analytics, hosting, and content management stacks for smarter research and lead generation. - [Byteforms](https://composio.dev/toolkits/byteforms) - Byteforms is an all-in-one platform for creating forms, managing submissions, and integrating data. It streamlines workflows by centralizing form data collection and automation. - [Cabinpanda](https://composio.dev/toolkits/cabinpanda) - Cabinpanda is a data collection platform for building and managing online forms. It helps streamline how you gather, organize, and analyze responses. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Influxdb cloud MCP? With a standalone Influxdb cloud MCP server, the agents and LLMs can only access a fixed set of Influxdb cloud tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Influxdb cloud and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Autogen? Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Influxdb cloud tools. ### Can I manage the permissions and scopes for Influxdb cloud while using Tool Router? Yes, absolutely. You can configure which Influxdb cloud scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Influxdb cloud data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)