# How to integrate Fireflies MCP with Pydantic AI ```json { "title": "How to integrate Fireflies MCP with Pydantic AI", "toolkit": "Fireflies", "toolkit_slug": "fireflies", "framework": "Pydantic AI", "framework_slug": "pydantic-ai", "url": "https://composio.dev/toolkits/fireflies/framework/pydantic-ai", "markdown_url": "https://composio.dev/toolkits/fireflies/framework/pydantic-ai.md", "updated_at": "2026-05-12T10:11:43.106Z" } ``` ## Introduction This guide walks you through connecting Fireflies to Pydantic AI using the Composio tool router. By the end, you'll have a working Fireflies agent that can transcribe this uploaded meeting audio file, summarize your last five recorded calls, list all transcripts involving the marketing team through natural language commands. This guide will help you understand how to give your Pydantic AI agent real control over a Fireflies account through Composio's Fireflies MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Fireflies with - [ChatGPT](https://composio.dev/toolkits/fireflies/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/fireflies/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fireflies/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fireflies/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fireflies/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fireflies/framework/codex) - [Cursor](https://composio.dev/toolkits/fireflies/framework/cursor) - [VS Code](https://composio.dev/toolkits/fireflies/framework/vscode) - [OpenCode](https://composio.dev/toolkits/fireflies/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/fireflies/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fireflies/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fireflies/framework/cli) - [Google ADK](https://composio.dev/toolkits/fireflies/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fireflies/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fireflies/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fireflies/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fireflies/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fireflies/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 Fireflies - 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 Fireflies 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 Fireflies MCP server, and what's possible with it? The Fireflies MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fireflies account. It provides structured and secure access to your voice conversations, so your agent can perform actions like transcribing meetings, summarizing discussions, searching transcripts, and managing audio files on your behalf. - Automated meeting transcription: Instantly upload audio files or add the Fireflies bot to live meetings so your agent can generate accurate transcripts for later review. - AI-powered conversation summarization: Let your agent fetch concise, actionable summaries of calls and meetings to help you quickly catch up or share insights with your team. - Transcript search and retrieval: Ask your agent to find specific transcripts or extract key segments from past conversations using keywords, dates, or participant names. - Audio file management: Effortlessly upload, organize, or delete audio files and transcripts right from your agent, keeping your conversation library up to date. - User and team insights: Enable your agent to fetch user details or team-wide meeting data, so you can stay on top of collaboration and engagement. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `FIREFLIES_ADD_TO_LIVE` | Add to Live Meeting | The AddToLive Action allows you to add the Fireflies.ai bot to an ongoing meeting. Note: This action requires a paid Fireflies plan to add bots to meetings. | | `FIREFLIES_CONTINUE_ASK_FRED_THREAD` | Continue AskFred Thread | Tool to continue an existing AskFred conversation thread with follow-up questions. This action CANNOT create new threads - it only works with existing thread IDs. Use when you need to maintain context from previous exchanges and ask additional questions about meeting data in the same conversation. To start a new thread, use FIREFLIES_CREATE_ASK_FRED_THREAD instead. | | `FIREFLIES_CREATE_ASK_FRED_THREAD` | Create AskFred Thread | Tool to start a new AskFred conversation thread with a question about meetings. Use when you need to query meeting transcripts using natural language, either for a specific meeting or across multiple meetings with filters. Supports time-based queries and participant-focused queries. | | `FIREFLIES_CREATE_BITE` | Create Bite | Tool to create a bite (short video or audio clip) from a transcript segment. Use when you need to extract a specific portion of a meeting recording with defined start and end times. | | `FIREFLIES_DELETE_TRANSCRIPT_BY_ID` | Delete Transcript by ID | Permanently delete a transcript from the Fireflies account by its unique ID. This is a destructive action that cannot be undone. The transcript, along with its associated audio/video files and summaries, will be permanently removed. Rate limited to 10 requests per minute across all user tiers. Verify the target transcript via FIREFLIES_GET_TRANSCRIPTS and obtain explicit user confirmation before calling this tool. | | `FIREFLIES_FETCH_AI_APP_OUTPUTS` | Fetch AI App Outputs | Tool to fetch AI App outputs for specific apps or transcripts. Use when you need to retrieve AI-generated results from Fireflies AI Apps for meetings. | | `FIREFLIES_GET_ASK_FRED_THREAD` | Get AskFred Thread | Tool to get a specific AskFred conversation thread with full history. Use when retrieving a particular AskFred thread along with all its messages and conversation details. | | `FIREFLIES_GET_ASK_FRED_THREADS` | Get AskFred Threads | Tool to retrieve a summary of all AskFred conversation threads for the current user. Use when you need to browse or list available AskFred conversations without fetching full message history. | | `FIREFLIES_GET_BITE_BY_ID` | Get Bite by ID | Fetches details for a specific bite by ID. Requires a Fireflies plan that supports Bites and appropriate API scope. If the bite is not found, use FIREFLIES_GET_TRANSCRIPT_BY_ID to retrieve full transcript context instead. | | `FIREFLIES_GET_BITES` | Get Transcripts | Fetches a list of bites (highlights) against input arguments. Bites are generated asynchronously after transcript completion — only call this after FIREFLIES_GET_TRANSCRIPT_BY_ID reports `status=completed`. Empty results are possible for valid meetings; use FIREFLIES_GET_TRANSCRIPT_BY_ID for full transcript context when bites are unavailable. | | `FIREFLIES_GET_TRANSCRIPT_BY_ID` | Get Transcript by ID | Fetches details for a specific Fireflies transcript ID. Requires a paid Fireflies plan. Response is nested at data.outputs.data.transcript; fields like sentences and attendees can be null — handle gracefully. transcript.summary.action_items may be a single newline-delimited string rather than an array — split by line breaks instead of iterating as an array. Limit concurrent calls to ~3 and apply exponential backoff on 429 responses, respecting Retry-After headers. | | `FIREFLIES_GET_TRANSCRIPTS` | Get Transcripts | Fetches a list of transcripts against input filters. Metadata filters (title, host_email, organizers, participants) match transcript metadata only, not spoken content. Pagination via skip/limit may trigger HTTP 429 on rapid requests; use backoff between pages. | | `FIREFLIES_GET_USER_BY_ID` | Get User by ID | The GetUser Action is designed to fetch details associated with a specific user id. | | `FIREFLIES_GET_USER_GROUPS` | Get User Groups | Tool to fetch a list of all user groups within the team with information about user groups including their members. Use when you need to retrieve team user groups, optionally filtering to only groups the current user belongs to with the mine parameter. | | `FIREFLIES_GET_USERS` | Get Users | Fetches a list of all users within the team, including their full email addresses. Use to resolve complete email addresses from user names before passing to tools that require exact email addresses (no partial addresses or domain-only values). | | `FIREFLIES_GRAPHQL_QUERY` | Execute GraphQL Query | Execute an authenticated, read-only Fireflies GraphQL operation (query) and return the full raw GraphQL response (data+errors) for reliable fallback and debugging. Use when higher-level tools fail due to schema mismatches or to access raw error details. | | `FIREFLIES_SET_USER_ROLE` | Set User Role | Tool to update a user's role within a team. Use when you need to grant or revoke admin privileges. Only team administrators can execute this action. Teams must maintain at least one admin member at all times. | | `FIREFLIES_UPDATE_MEETING_CHANNEL` | Update Meeting Channel | Tool to batch update channel assignments for 1-5 meeting transcripts. Use when you need to assign meetings to a specific channel. Requires meeting owner or team admin privileges. All-or-nothing semantics: if any transcript fails validation, none are updated. | | `FIREFLIES_UPDATE_MEETING_PRIVACY` | Update Meeting Privacy | Tool to update the privacy setting of a meeting transcript. Use when you need to change meeting access permissions. Only meeting owners and team admins can update privacy settings. | | `FIREFLIES_UPDATE_MEETING_TITLE` | Update Meeting Title | Tool to update the title of a meeting transcript. Use when you need to rename a meeting. Requires admin privileges and the meeting owner must be in your team. | | `FIREFLIES_UPLOAD_AUDIO` | Upload Audio | The UploadAudio Action allows you to upload audio files to Fireflies.ai for transcription. Transcription is asynchronous — after submission, results may take several minutes to become available; use transcript retrieval tools to poll for completion. Note: This action requires a paid Fireflies plan to upload and transcribe audio files. | ## Supported Triggers | Trigger slug | Name | Description | |---|---|---| | `FIREFLIES_TRANSCRIPTION_COMPLETE` | Transcription Complete Trigger | Triggers when a transcription is complete. Polls Fireflies API for transcripts created or updated since the last poll time. Handles rate limiting gracefully. | ## Creating MCP Server - Stand-alone vs Composio SDK The Fireflies MCP server is an implementation of the Model Context Protocol that connects your AI agent to Fireflies. It provides structured and secure access so your agent can perform Fireflies 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 Fireflies - 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 Fireflies - MCPServerStreamableHTTP connects to the Fireflies 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 Fireflies 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 Fireflies composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["fireflies"], ) 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 Fireflies endpoint - The agent uses GPT-5 to interpret user commands and perform Fireflies operations - The instructions field defines the agent's role and behavior ```python # Attach the MCP server to a Pydantic AI Agent fireflies_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[fireflies_mcp], instructions=( "You are a Fireflies assistant. Use Fireflies 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 - Fireflies 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 Fireflies.\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 Fireflies composio = Composio(api_key=api_key) session = composio.create( user_id=user_id, toolkits=["fireflies"], ) 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 fireflies_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY}) agent = Agent( "openai:gpt-5", toolsets=[fireflies_mcp], instructions=( "You are a Fireflies assistant. Use Fireflies 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 Fireflies.\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 Fireflies through Composio's Tool Router. With this setup, your agent can perform real Fireflies 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 + Fireflies 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 Fireflies MCP Agent with another framework - [ChatGPT](https://composio.dev/toolkits/fireflies/framework/chatgpt) - [OpenAI Agents SDK](https://composio.dev/toolkits/fireflies/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/fireflies/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/fireflies/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/fireflies/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/fireflies/framework/codex) - [Cursor](https://composio.dev/toolkits/fireflies/framework/cursor) - [VS Code](https://composio.dev/toolkits/fireflies/framework/vscode) - [OpenCode](https://composio.dev/toolkits/fireflies/framework/opencode) - [OpenClaw](https://composio.dev/toolkits/fireflies/framework/openclaw) - [Hermes](https://composio.dev/toolkits/fireflies/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/fireflies/framework/cli) - [Google ADK](https://composio.dev/toolkits/fireflies/framework/google-adk) - [LangChain](https://composio.dev/toolkits/fireflies/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/fireflies/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/fireflies/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/fireflies/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/fireflies/framework/crew-ai) ## Related Toolkits - [Firecrawl](https://composio.dev/toolkits/firecrawl) - Firecrawl automates large-scale web crawling and data extraction. It helps organizations efficiently gather, index, and analyze content from online sources. - [Tavily](https://composio.dev/toolkits/tavily) - Tavily offers powerful search and data retrieval from documents, databases, and the web. It helps teams locate and filter information instantly, saving hours on research. - [Exa](https://composio.dev/toolkits/exa) - Exa is a data extraction and search platform for gathering and analyzing information from websites, APIs, or databases. It helps teams quickly surface insights and automate data-driven workflows. - [Serpapi](https://composio.dev/toolkits/serpapi) - SerpApi is a real-time API for structured search engine results. It lets you automate SERP data collection, parsing, and analysis for SEO and research. - [Peopledatalabs](https://composio.dev/toolkits/peopledatalabs) - Peopledatalabs delivers B2B data enrichment and identity resolution APIs. Supercharge your apps with accurate, up-to-date business and contact data. - [Snowflake](https://composio.dev/toolkits/snowflake) - Snowflake is a cloud data warehouse built for elastic scaling, secure data sharing, and fast SQL analytics across major clouds. - [Posthog](https://composio.dev/toolkits/posthog) - PostHog is an open-source analytics platform for tracking user interactions and product metrics. It helps teams refine features, analyze funnels, and reduce churn with actionable insights. - [Amplitude](https://composio.dev/toolkits/amplitude) - Amplitude is a digital analytics platform for product and behavioral data insights. It helps teams analyze user journeys and make data-driven decisions quickly. - [Bright Data MCP](https://composio.dev/toolkits/brightdata_mcp) - Bright Data MCP is an AI-powered web scraping and data collection platform. Instantly access public web data in real time with advanced scraping tools. - [Browseai](https://composio.dev/toolkits/browseai) - Browseai is a web automation and data extraction platform that turns any website into an API. It's perfect for monitoring websites and retrieving structured data without manual scraping. - [ClickHouse](https://composio.dev/toolkits/clickhouse) - ClickHouse is an open-source, column-oriented database for real-time analytics and big data processing using SQL. Its lightning-fast query performance makes it ideal for handling large datasets and delivering instant insights. - [Coinmarketcal](https://composio.dev/toolkits/coinmarketcal) - CoinMarketCal is a community-powered crypto calendar for upcoming events, announcements, and releases. It helps traders track market-moving developments and stay ahead in the crypto space. - [Control d](https://composio.dev/toolkits/control_d) - Control d is a customizable DNS filtering and traffic redirection platform. It helps you manage internet access, enforce policies, and monitor usage across devices and networks. - [Databox](https://composio.dev/toolkits/databox) - Databox is a business analytics platform that connects your data from any tool and device. It helps you track KPIs, build dashboards, and discover actionable insights. - [Databricks](https://composio.dev/toolkits/databricks) - Databricks is a unified analytics platform for big data and AI on the lakehouse architecture. It empowers data teams to collaborate, analyze, and build scalable solutions efficiently. - [Datagma](https://composio.dev/toolkits/datagma) - Datagma delivers data intelligence and analytics for business growth and market discovery. Get actionable market insights and track competitors to inform your strategy. - [Delighted](https://composio.dev/toolkits/delighted) - Delighted is a customer feedback platform based on the Net Promoter System®. It helps you quickly gather, track, and act on customer sentiment. - [Dovetail](https://composio.dev/toolkits/dovetail) - Dovetail is a research analysis platform for transcript review and insight generation. It helps teams code interviews, analyze feedback, and create actionable research summaries. - [Dub](https://composio.dev/toolkits/dub) - Dub is a short link management platform with analytics and API access. Use it to easily create, manage, and track branded short links for your business. - [Elasticsearch](https://composio.dev/toolkits/elasticsearch) - Elasticsearch is a distributed, RESTful search and analytics engine for all types of data. It delivers fast, scalable search and powerful analytics across massive datasets. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Fireflies MCP? With a standalone Fireflies MCP server, the agents and LLMs can only access a fixed set of Fireflies tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Fireflies 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 Fireflies tools. ### Can I manage the permissions and scopes for Fireflies while using Tool Router? Yes, absolutely. You can configure which Fireflies 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 Fireflies data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)