# How to integrate Spotlightr MCP with Autogen ```json { "title": "How to integrate Spotlightr MCP with Autogen", "toolkit": "Spotlightr", "toolkit_slug": "spotlightr", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/spotlightr/framework/autogen", "markdown_url": "https://composio.dev/toolkits/spotlightr/framework/autogen.md", "updated_at": "2026-05-12T10:26:57.888Z" } ``` ## Introduction This guide walks you through connecting Spotlightr to AutoGen using the Composio tool router. By the end, you'll have a working Spotlightr agent that can show me your top 5 most viewed videos, get viewer engagement stats for video id 1234, list videos with highest watch time this week through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Spotlightr account through Composio's Spotlightr MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Spotlightr with - [OpenAI Agents SDK](https://composio.dev/toolkits/spotlightr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/spotlightr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/spotlightr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/spotlightr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/spotlightr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/spotlightr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/spotlightr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/spotlightr/framework/cli) - [Google ADK](https://composio.dev/toolkits/spotlightr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/spotlightr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/spotlightr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/spotlightr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/spotlightr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/spotlightr/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 Spotlightr - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Spotlightr tools - Run a live chat loop where you ask the agent to perform Spotlightr 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 Spotlightr MCP server, and what's possible with it? The Spotlightr MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Spotlightr account. It provides structured and secure access to your video content and analytics, so your agent can perform actions like retrieving top-performing videos, accessing detailed video metrics, and surfacing engagement insights automatically on your behalf. - Top video discovery and listing: Instantly ask your agent to fetch and list your most viewed or highest performing Spotlightr videos. - Video analytics and metrics retrieval: Have your agent pull comprehensive analytics for a specific video, including views, engagement rates, unique viewers, and total watch time. - Engagement insight extraction: Let the agent surface actionable insights about viewer engagement for any video, making it easy to spot trends and opportunities. - Automated reporting support: Your agent can collect and summarize video performance data, making regular reporting and decision-making faster and more data-driven. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `SPOTLIGHTR_ADD_DOMAIN` | Add Domain | Tool to add a whitelisted domain for embedding Spotlightr videos. Use when you need to whitelist a new domain for video embedding. | | `SPOTLIGHTR_CREATE_GROUP` | Create Group | Tool to create a new project (group) in Spotlightr. Use when you need to organize videos into a new project or group. | | `SPOTLIGHTR_CREATE_VIDEO` | Create Video | Tool to create a video in Spotlightr by uploading a file or linking from an external source. Use when you need to add a new video from YouTube, Google Drive, Vimeo, or other sources. | | `SPOTLIGHTR_DELETE_VIDEO` | Delete Video | Tool to delete or remove videos from Spotlightr projects. Use when you need to permanently delete one or more videos by their IDs. | | `SPOTLIGHTR_GET_DOMAINS` | Get Domains | Tool to retrieve whitelisted domains for a Spotlightr account. Use when you need to list all domains approved for embedding videos. | | `SPOTLIGHTR_GET_TOP_VIDEOS` | Get Top Videos | Tool to retrieve the top videos from a Spotlightr account. Use when you need to list the most viewed videos. | | `SPOTLIGHTR_GET_VIDEO_METRICS` | Get Video Metrics | Tool to retrieve analytics metrics for a specified video. Use when you have a video ID and need its metrics (loads, plays, playRate, completionRate, shares, etc.). | | `SPOTLIGHTR_GET_VIDEO_SOURCE` | Get Video Source | Tool to get or replace the video source for an existing video in Spotlightr. Use when you need to update a video's source URL. | | `SPOTLIGHTR_GET_VIDEO_VIEWS` | Get Video Views | Tool to retrieve video view data with optional filtering by viewer ID and watch status. Use when you need detailed view records for a specific video. | | `SPOTLIGHTR_LIST_GROUPS` | List Groups | Tool to retrieve all projects (groups) in a Spotlightr account. Use when you need to list all available projects or groups. | | `SPOTLIGHTR_LIST_VIDEOS` | List Videos | Tool to retrieve videos from a Spotlightr account. Use when you need to list all videos or filter by specific video ID or project. | | `SPOTLIGHTR_SEARCH_GLOBAL` | Search Global | Tool to perform account-wide search across all videos and content in Spotlightr. Use when you need to find specific videos, projects, or content by name or keyword. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Spotlightr MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Spotlightr. Instead of manually wiring Spotlightr 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 Spotlightr 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 Spotlightr 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 Spotlightr 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 Spotlightr 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 Spotlightr session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["spotlightr"] ) 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 Spotlightr 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 Spotlightr assistant agent with MCP tools agent = AssistantAgent( name="spotlightr_assistant", description="An AI assistant that helps with Spotlightr 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 Spotlightr 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 Spotlightr 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 Spotlightr session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["spotlightr"] ) 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 Spotlightr assistant agent with MCP tools agent = AssistantAgent( name="spotlightr_assistant", description="An AI assistant that helps with Spotlightr 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 Spotlightr 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 Spotlightr 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 Spotlightr, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Spotlightr MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/spotlightr/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/spotlightr/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/spotlightr/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/spotlightr/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/spotlightr/framework/codex) - [OpenClaw](https://composio.dev/toolkits/spotlightr/framework/openclaw) - [Hermes](https://composio.dev/toolkits/spotlightr/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/spotlightr/framework/cli) - [Google ADK](https://composio.dev/toolkits/spotlightr/framework/google-adk) - [LangChain](https://composio.dev/toolkits/spotlightr/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/spotlightr/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/spotlightr/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/spotlightr/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/spotlightr/framework/crew-ai) ## Related Toolkits - [Reddit](https://composio.dev/toolkits/reddit) - Reddit is a social news platform with thriving user-driven communities (subreddits). It's the go-to place for discussion, content sharing, and viral marketing. - [Facebook](https://composio.dev/toolkits/facebook) - Facebook is a social media and advertising platform for businesses and creators. It helps you connect, share, and manage content across your public Facebook Pages. - [Linkedin](https://composio.dev/toolkits/linkedin) - LinkedIn is a professional networking platform for connecting, sharing content, and engaging with business opportunities. It's the go-to place for building your professional brand and unlocking new career connections. - [Active campaign](https://composio.dev/toolkits/active_campaign) - ActiveCampaign is a marketing automation and CRM platform for managing email campaigns, sales pipelines, and customer segmentation. It helps businesses engage customers and drive growth through smart automation and targeted outreach. - [ActiveTrail](https://composio.dev/toolkits/active_trail) - ActiveTrail is a user-friendly email marketing and automation platform. It helps you reach subscribers and automate campaigns with ease. - [Ahrefs](https://composio.dev/toolkits/ahrefs) - Ahrefs is an SEO and marketing platform for site audits, keyword research, and competitor insights. It helps you improve search rankings and drive organic traffic. - [Amcards](https://composio.dev/toolkits/amcards) - AMCards lets you create and mail personalized greeting cards online. Build stronger customer relationships with easy, automated card campaigns. - [Beamer](https://composio.dev/toolkits/beamer) - Beamer is a news and changelog platform for in-app announcements and feature updates. It helps companies boost user engagement by sharing news where users are most active. - [Benchmark email](https://composio.dev/toolkits/benchmark_email) - Benchmark Email is a platform for creating, sending, and tracking email campaigns. It's built to help you engage audiences and analyze results—all in one place. - [Bigmailer](https://composio.dev/toolkits/bigmailer) - BigMailer is an email marketing platform for managing multiple brands with white-labeling and automation. It helps teams streamline campaigns and simplify integration with Amazon SES. - [Brandfetch](https://composio.dev/toolkits/brandfetch) - Brandfetch is an API that delivers company logos, colors, and visual branding assets. It helps marketers and developers keep brand visuals consistent everywhere. - [Brevo](https://composio.dev/toolkits/brevo) - Brevo is an all-in-one email and SMS marketing platform for transactional messaging, automation, and CRM. It helps businesses engage customers and streamline communications through powerful campaign tools. - [Campayn](https://composio.dev/toolkits/campayn) - Campayn is an email marketing platform for creating, sending, and managing campaigns. It helps businesses engage contacts and grow audiences with easy-to-use tools. - [Cardly](https://composio.dev/toolkits/cardly) - Cardly is a platform for creating and sending personalized direct mail to customers. It helps businesses break through the digital clutter by getting real engagement via physical mailboxes. - [ClickSend](https://composio.dev/toolkits/clicksend) - ClickSend is a cloud-based SMS and email marketing platform for businesses. It streamlines communication by enabling quick message delivery and contact management. - [Crustdata](https://composio.dev/toolkits/crustdata) - CrustData is an AI-powered data intelligence platform for real-time company and people data. It helps B2B sales teams, AI SDRs, and investors react to live business signals. - [Curated](https://composio.dev/toolkits/curated) - Curated is a platform for collecting, curating, and publishing newsletters. It streamlines content aggregation and distribution for creators and teams. - [Customerio](https://composio.dev/toolkits/customerio) - Customer.io is a customer engagement platform for targeted messaging across email, SMS, and push. Easily automate, segment, and track communications with your audience. - [Cutt ly](https://composio.dev/toolkits/cutt_ly) - Cutt.ly is a URL shortening service for managing and analyzing links. Streamline your workflows with quick, trackable, and branded short URLs. - [Demio](https://composio.dev/toolkits/demio) - Demio is webinar software built for marketers, offering both live and automated sessions with interactive features. It helps teams engage audiences and optimize lead generation through detailed analytics. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Spotlightr MCP? With a standalone Spotlightr MCP server, the agents and LLMs can only access a fixed set of Spotlightr tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Spotlightr 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 Spotlightr tools. ### Can I manage the permissions and scopes for Spotlightr while using Tool Router? Yes, absolutely. You can configure which Spotlightr 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 Spotlightr data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)