# How to integrate Deepgram MCP with Mastra AI ```json { "title": "How to integrate Deepgram MCP with Mastra AI", "toolkit": "Deepgram", "toolkit_slug": "deepgram", "framework": "Mastra AI", "framework_slug": "mastra-ai", "url": "https://composio.dev/toolkits/deepgram/framework/mastra-ai", "markdown_url": "https://composio.dev/toolkits/deepgram/framework/mastra-ai.md", "updated_at": "2026-05-12T10:08:28.462Z" } ``` ## Introduction This guide walks you through connecting Deepgram to Mastra AI using the Composio tool router. By the end, you'll have a working Deepgram agent that can transcribe meeting audio from this url, summarize podcast episode for quick review, convert blog post text to speech audio through natural language commands. This guide will help you understand how to give your Mastra AI agent real control over a Deepgram account through Composio's Deepgram MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Deepgram with - [OpenAI Agents SDK](https://composio.dev/toolkits/deepgram/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepgram/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepgram/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepgram/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepgram/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepgram/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepgram/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepgram/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepgram/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepgram/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepgram/framework/ai-sdk) - [LlamaIndex](https://composio.dev/toolkits/deepgram/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepgram/framework/crew-ai) ## TL;DR Here's what you'll learn: - Set up your environment so Mastra, OpenAI, and Composio work together - Create a Tool Router session in Composio that exposes Deepgram tools - Connect Mastra's MCP client to the Composio generated MCP URL - Fetch Deepgram tool definitions and attach them as a toolset - Build a Mastra agent that can reason, call tools, and return structured results - Run an interactive CLI where you can chat with your Deepgram agent ## What is Mastra AI? Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP. Key features include: - MCP Client: Built-in support for Model Context Protocol servers - Toolsets: Organize tools into logical groups - Step Callbacks: Monitor and debug agent execution - OpenAI Integration: Works with OpenAI models via @ai-sdk/openai ## What is the Deepgram MCP server, and what's possible with it? The Deepgram MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Deepgram account. It provides structured and secure access to Deepgram's speech AI services, so your agent can transcribe audio, generate summaries, convert text to speech, detect topics, and analyze project usage on your behalf. - Automated audio transcription: Your agent can transcribe pre-recorded audio files into accurate, readable text for effortless documentation and analysis. - Audio summarization: Have your agent generate quick, concise summaries of audio content, making it easy to review lengthy recordings in seconds. - Natural-sounding text-to-speech: Let your agent convert written text into lifelike speech audio using Deepgram's public TTS models for accessibility or creative applications. - Topic detection from audio: Enable your agent to identify and report on the main topics discussed within audio files, streamlining content review and categorization. - Usage analytics and project insights: Retrieve and analyze project usage metrics, scope permissions, and available models to optimize your Deepgram integration and monitor account health. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DEEPGRAM_GET_MODEL` | Get Model by ID | Retrieve metadata for a specific Deepgram model by its UUID. Returns detailed model information including name, architecture, supported languages, version, and capabilities. Works for both STT (speech-to-text) and TTS (text-to-speech) models. | | `DEEPGRAM_GET_MODELS` | Get Public Models | Retrieve metadata on all public Deepgram models (speech-to-text and text-to-speech). Returns comprehensive model information including supported languages, architectures, versions, and capabilities. Set include_outdated to True to include deprecated versions. | | `DEEPGRAM_GET_PROJECTS` | List Deepgram Projects | Tool to list all Deepgram projects. Use after authenticating with your API key. | | `DEEPGRAM_GET_PROJECT_USAGE_SUMMARY` | Get Project Usage Summary | Retrieves aggregated usage statistics for a Deepgram project including total audio duration, billable duration, number of requests, channels processed, and confidence/relevance scores. Returns both overall totals and breakdowns by model/accessor/tag. Use this to analyze API consumption, track costs, or monitor transcription quality metrics over time. | | `DEEPGRAM_LIST_PROJECT_SCOPES` | List Project Scopes | Tool to list all scopes for a specified Deepgram project. Use when you need to retrieve all permission scopes for a project. | | `DEEPGRAM_LIST_THINK_MODELS` | List Think Models | Tool to list available think models for AI agent processing and voice agent configuration. Use when you need to see which think models are available for voice agents. | | `DEEPGRAM_SPEECH_TO_TEXT_PRE_RECORDED` | Transcribe Pre-recorded Audio | Tool to transcribe pre-recorded audio files into text. Use when converting a publicly accessible audio file URL to text. Primary transcript is at `results.channels[0].alternatives[0].transcript` in the response. Silent audio returns a valid empty transcript, not an error. Verify supported models and language codes via `DEEPGRAM_GET_MODELS` when uncertain. | | `DEEPGRAM_TEXT_TO_SPEECH_REST` | Text-to-Speech (REST) | Tool to convert text into natural-sounding speech. Use when you need TTS audio from text inputs. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Deepgram MCP server is an implementation of the Model Context Protocol that connects your AI agent to Deepgram. It provides structured and secure access so your agent can perform Deepgram 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: - Node.js 18 or higher - A Composio account with an active API key - An OpenAI API key - Basic familiarity with TypeScript ### 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 need credits or a connected billing setup to use the models. - Store the key somewhere safe. Composio API Key - Log in to the [Composio dashboard](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Go to Settings and copy your API key. - This key lets your Mastra agent talk to Composio and reach Deepgram through MCP. ### 2. Install dependencies Install the required packages. What's happening: - @composio/core is the Composio SDK for creating MCP sessions - @mastra/core provides the Agent class - @mastra/mcp is Mastra's MCP client - @ai-sdk/openai is the model wrapper for OpenAI - dotenv loads environment variables from .env ```bash npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's happening: - COMPOSIO_API_KEY authenticates your requests to Composio - COMPOSIO_USER_ID tells Composio which user this session belongs to - OPENAI_API_KEY lets the Mastra agent call OpenAI models ```bash COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here OPENAI_API_KEY=your_openai_api_key_here ``` ### 4. Import libraries and validate environment What's happening: - dotenv/config auto loads your .env so process.env.* is available - openai gives you a Mastra compatible model wrapper - Agent is the Mastra agent that will call tools and produce answers - MCPClient connects Mastra to your Composio MCP server - Composio is used to create a Tool Router session ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Agent } from "@mastra/core/agent"; import { MCPClient } from "@mastra/mcp"; import { Composio } from "@composio/core"; import * as readline from "readline"; import type { AiMessageType } from "@mastra/core/agent"; const openaiAPIKey = process.env.OPENAI_API_KEY; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey as string, }); ``` ### 5. Create a Tool Router session for Deepgram What's happening: - create spins up a short-lived MCP HTTP endpoint for this user - The toolkits array contains "deepgram" for Deepgram access - session.mcp.url is the MCP URL that Mastra's MCPClient will connect to ```typescript async function main() { const session = await composio.create( composioUserID as string, { toolkits: ["deepgram"], }, ); const composioMCPUrl = session.mcp.url; console.log("Deepgram MCP URL:", composioMCPUrl); ``` ### 6. Configure Mastra MCP client and fetch tools What's happening: - MCPClient takes an id for this client and a list of MCP servers - The headers property includes the x-api-key for authentication - getTools fetches the tool definitions exposed by the Deepgram toolkit ```typescript const mcpClient = new MCPClient({ id: composioUserID as string, servers: { nasdaq: { url: new URL(composioMCPUrl), requestInit: { headers: session.mcp.headers, }, }, }, timeout: 30_000, }); console.log("Fetching MCP tools from Composio..."); const composioTools = await mcpClient.getTools(); console.log("Number of tools:", Object.keys(composioTools).length); ``` ### 7. Create the Mastra agent What's happening: - Agent is the core Mastra agent - name is just an identifier for logging and debugging - instructions guide the agent to use tools instead of only answering in natural language - model uses openai("gpt-5") to configure the underlying LLM ```typescript const agent = new Agent({ name: "deepgram-mastra-agent", instructions: "You are an AI agent with Deepgram tools via Composio.", model: "openai/gpt-5", }); ``` ### 8. Set up interactive chat interface What's happening: - messages keeps the full conversation history in Mastra's expected format - agent.generate runs the agent with conversation history and Deepgram toolsets - maxSteps limits how many tool calls the agent can take in a single run - onStepFinish is a hook that prints intermediate steps for debugging ```typescript let messages: AiMessageType[] = []; console.log("Chat started! Type 'exit' or 'quit' to end.\n"); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (userInput: string) => { const trimmedInput = userInput.trim(); if (["exit", "quit", "bye"].includes(trimmedInput.toLowerCase())) { console.log("\nGoodbye!"); rl.close(); process.exit(0); } if (!trimmedInput) { rl.prompt(); return; } messages.push({ id: crypto.randomUUID(), role: "user", content: trimmedInput, }); console.log("\nAgent is thinking...\n"); try { const response = await agent.generate(messages, { toolsets: { deepgram: composioTools, }, maxSteps: 8, }); const { text } = response; if (text && text.trim().length > 0) { console.log(`Agent: ${text}\n`); messages.push({ id: crypto.randomUUID(), role: "assistant", content: text, }); } } catch (error) { console.error("\nError:", error); } rl.prompt(); }); rl.on("close", async () => { console.log("\nSession ended."); await mcpClient.disconnect(); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Complete Code ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Agent } from "@mastra/core/agent"; import { MCPClient } from "@mastra/mcp"; import { Composio } from "@composio/core"; import * as readline from "readline"; import type { AiMessageType } from "@mastra/core/agent"; const openaiAPIKey = process.env.OPENAI_API_KEY; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!openaiAPIKey) throw new Error("OPENAI_API_KEY is not set"); if (!composioAPIKey) throw new Error("COMPOSIO_API_KEY is not set"); if (!composioUserID) throw new Error("COMPOSIO_USER_ID is not set"); const composio = new Composio({ apiKey: composioAPIKey as string }); async function main() { const session = await composio.create(composioUserID as string, { toolkits: ["deepgram"], }); const composioMCPUrl = session.mcp.url; const mcpClient = new MCPClient({ id: composioUserID as string, servers: { deepgram: { url: new URL(composioMCPUrl), requestInit: { headers: session.mcp.headers, }, }, }, timeout: 30_000, }); const composioTools = await mcpClient.getTools(); const agent = new Agent({ name: "deepgram-mastra-agent", instructions: "You are an AI agent with Deepgram tools via Composio.", model: "openai/gpt-5", }); let messages: AiMessageType[] = []; const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); rl.on("line", async (input: string) => { const trimmed = input.trim(); if (["exit", "quit"].includes(trimmed.toLowerCase())) { rl.close(); return; } messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed }); const { text } = await agent.generate(messages, { toolsets: { deepgram: composioTools }, maxSteps: 8, }); if (text) { console.log(`Agent: ${text}\n`); messages.push({ id: crypto.randomUUID(), role: "assistant", content: text }); } rl.prompt(); }); rl.on("close", async () => { await mcpClient.disconnect(); process.exit(0); }); } main(); ``` ## Conclusion You've built a Mastra AI agent that can interact with Deepgram through Composio's Tool Router. You can extend this further by: - Adding other toolkits like Gmail, Slack, or GitHub - Building a web-based chat interface around this agent - Using multiple MCP endpoints to enable cross-app workflows ## How to build Deepgram MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/deepgram/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/deepgram/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/deepgram/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/deepgram/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/deepgram/framework/codex) - [OpenClaw](https://composio.dev/toolkits/deepgram/framework/openclaw) - [Hermes](https://composio.dev/toolkits/deepgram/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/deepgram/framework/cli) - [Google ADK](https://composio.dev/toolkits/deepgram/framework/google-adk) - [LangChain](https://composio.dev/toolkits/deepgram/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/deepgram/framework/ai-sdk) - [LlamaIndex](https://composio.dev/toolkits/deepgram/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/deepgram/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Deepgram MCP? With a standalone Deepgram MCP server, the agents and LLMs can only access a fixed set of Deepgram tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Deepgram and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Mastra AI? Yes, you can. Mastra 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 Deepgram tools. ### Can I manage the permissions and scopes for Deepgram while using Tool Router? Yes, absolutely. You can configure which Deepgram 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 Deepgram data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)