# How to integrate Influxdb cloud MCP with Pydantic AI ```json { "title": "How to integrate Influxdb cloud MCP with Pydantic AI", "toolkit": "Influxdb cloud", "toolkit_slug": "influxdb_cloud", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/influxdb_cloud/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/influxdb_cloud/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:15:53.118Z" } ``` ## Introduction This guide walks you through connecting Influxdb cloud to Pydantic AI 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 Pydantic AI 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: - How to set up your Composio API key and User ID - How to create a Composio Tool Router session for Influxdb cloud - How to attach an MCP Server to a Pydantic AI agent - How to stream responses and maintain chat history - How to build a simple REPL-style chat interface to test your Influxdb cloud workflows ## What is Pydantic AI? Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications. Key features include: - Type Safety: Built on Pydantic for automatic data validation - MCP Support: Native support for Model Context Protocol servers - Streaming: Built-in support for streaming responses - Async First: Designed for async/await patterns ## 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 agent to Influxdb cloud. It provides structured and secure access so your agent can perform Influxdb cloud operations on your behalf through a secure, permission-based interface. 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 Before starting, make sure you have: - Python 3.9 or higher - A Composio account with an active API key - Basic familiarity with Python and async programming ### 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 the required libraries. What's happening: - composio connects your agent to external SaaS tools like Influxdb cloud - pydantic-ai lets you create structured AI agents with tool support - python-dotenv loads your environment variables securely from a .env file ```bash pip install composio pydantic-ai python-dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your agent to Composio's API - USER_ID associates your session with your account for secure tool access - OPENAI_API_KEY to access OpenAI LLMs ```bash COMPOSIO_API_KEY=your_composio_api_key_here USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key ``` ### 4. Import dependencies What's happening: - We load environment variables and import required modules - Composio manages connections to Influxdb cloud - MCPServerStreamableHTTP connects to the Influxdb cloud MCP server endpoint - Agent from Pydantic AI lets you define and run the AI assistant ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() ``` ### 5. Create a Tool Router Session What's happening: - We're creating a Tool Router session that gives your agent access to Influxdb cloud tools - The create method takes the user ID and specifies which toolkits should be available - The returned session.mcp.url is the MCP server URL that your agent will use ```python async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Influxdb cloud composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["influxdb_cloud"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") ``` ### 6. Initialize the Pydantic AI Agent What's happening: - The MCP client connects to the Influxdb cloud endpoint - The agent uses GPT-5 to interpret user commands and perform Influxdb cloud operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent influxdb_cloud_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[influxdb_cloud_mcp], instructions=( "You are a Influxdb cloud assistant. Use Influxdb cloud tools to help users " "with their requests. Ask clarifying questions when needed." ), ) ``` ### 7. Build the chat interface What's happening: - The agent reads input from the terminal and streams its response - Influxdb cloud API calls happen automatically under the hood - The model keeps conversation history to maintain context across turns ```python # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Influxdb cloud.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() ``` ### 8. Run the application What's happening: - The asyncio loop launches the agent and keeps it running until you exit ```python if __name__ == "__main__": asyncio.run(main()) ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerStreamableHTTP load_dotenv() async def main(): api_key = os.getenv("COMPOSIO_API_KEY") user_id = os.getenv("USER_ID") if not api_key or not user_id: raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment") # Create a Composio Tool Router session for Influxdb cloud composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["influxdb_cloud"], ) url = session.mcp.url if not url: raise ValueError("Composio session did not return an MCP URL") # Attach the MCP server to a Pydantic AI Agent influxdb_cloud_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[influxdb_cloud_mcp], instructions=( "You are a Influxdb cloud assistant. Use Influxdb cloud tools to help users " "with their requests. Ask clarifying questions when needed." ), ) # Simple REPL with message history history = [] print("Chat started! Type 'exit' or 'quit' to end.\n") print("Try asking the agent to help you with Influxdb cloud.\n") 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", flush=True) async with agent.run_stream(user_input, message_history=history) as stream_result: collected_text = "" async for chunk in stream_result.stream_output(): text_piece = None if isinstance(chunk, str): text_piece = chunk elif hasattr(chunk, "delta") and isinstance(chunk.delta, str): text_piece = chunk.delta elif hasattr(chunk, "text"): text_piece = chunk.text if text_piece: collected_text += text_piece result = stream_result print(f"Agent: {collected_text}\n") history = result.all_messages() if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You've built a Pydantic AI agent that can interact with Influxdb cloud through Composio's Tool Router. With this setup, your agent can perform real Influxdb cloud actions through natural language. You can extend this further by: - Adding other toolkits like Gmail, HubSpot, or Salesforce - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Influxdb cloud for workflow automation) This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations. ## 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. It's the go-to tool for calculations, reporting, and flexible data management. - [21risk](https://composio.dev/toolkits/_21risk) - 21RISK is a web app built for easy checklist, audit, and compliance management. It streamlines risk processes so teams can focus on what matters. - [Abstract](https://composio.dev/toolkits/abstract) - Abstract provides a suite of APIs for automating data validation and enrichment tasks. It helps developers streamline workflows and ensure data quality with minimal effort. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agenty](https://composio.dev/toolkits/agenty) - Agenty is a web scraping and automation platform for extracting data and automating browser tasks—no coding needed. 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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 Pydantic AI? Yes, you can. Pydantic AI 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)