# How to integrate Giphy MCP with LlamaIndex ```json { "title": "How to integrate Giphy MCP with LlamaIndex", "toolkit": "Giphy", "toolkit_slug": "giphy", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/giphy/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/giphy/framework/llama-index.md", "updated_at": "2026-05-12T10:12:59.307Z" } ``` ## Introduction This guide walks you through connecting Giphy to LlamaIndex using the Composio tool router. By the end, you'll have a working Giphy agent that can find trending cat gifs for today, get sticker variations for smile emoji, list gifs in the 'reactions' category through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Giphy account through Composio's Giphy MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Giphy with - [OpenAI Agents SDK](https://composio.dev/toolkits/giphy/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/giphy/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/giphy/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/giphy/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/giphy/framework/codex) - [OpenClaw](https://composio.dev/toolkits/giphy/framework/openclaw) - [Hermes](https://composio.dev/toolkits/giphy/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/giphy/framework/cli) - [Google ADK](https://composio.dev/toolkits/giphy/framework/google-adk) - [LangChain](https://composio.dev/toolkits/giphy/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/giphy/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/giphy/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/giphy/framework/crew-ai) ## TL;DR Here's what you'll learn: - Set your OpenAI and Composio API keys - Install LlamaIndex and Composio packages - Create a Composio Tool Router session for Giphy - Connect LlamaIndex to the Giphy MCP server - Build a Giphy-powered agent using LlamaIndex - Interact with Giphy through natural language ## What is LlamaIndex? LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools. Key features include: - ReAct Agent: Reasoning and acting pattern for tool-using agents - MCP Tools: Native support for Model Context Protocol - Context Management: Maintain conversation context across interactions - Async Support: Built for async/await patterns ## What is the Giphy MCP server, and what's possible with it? The Giphy MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Giphy account. It provides structured and secure access to the world’s largest GIF and sticker library, so your agent can search for GIFs, fetch trending categories, retrieve GIF metadata, and even track analytics on user interactions automatically. - GIF and sticker search and retrieval: Instantly have your agent fetch GIFs and stickers by ID, category, or emoji for any topic or mood you need. - Browse trending categories and curated content: Let your agent pull the latest GIF categories and browse curated collections to suggest the perfect GIF for any occasion. - Access detailed GIF and sticker metadata: Retrieve comprehensive information about specific GIFs, stickers, or even groups of items by their unique IDs. - Emoji and sticker variation discovery: Explore emoji GIFs and their creative variations, making it easy to add fun reactions or flair to your app or chat. - User interaction analytics logging: Track and register when users view, click, or share GIFs, enabling smarter personalization and reporting within your workflows. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `GIPHY_ACTION_REGISTER` | Giphy Analytics Register | Tool to register user interactions (view, click, send) with a GIF for analytics. Use when you have the analytics payload, timestamp, and user ID to record the event. | | `GIPHY_CATEGORIES` | GIPHY Categories | Tool to fetch a list of GIF categories on GIPHY. Use when you need to browse available categories before selecting one. | | `GIPHY_CATEGORY_BY_ID` | GIPHY: Get Category by ID | Tool to fetch metadata for a GIF category by its unique ID. Use when you need details for a specific category identifier. | | `GIPHY_CATEGORY_GIFS` | GIPHY: Category GIFs | Tool to fetch GIFs associated with a specific GIF category. Use after retrieving category_id from the categories endpoint to get curated GIFs. | | `GIPHY_EMOJI` | GIPHY Emoji | Tool to fetch GIPHY emoji GIF objects. Use when you need a paginated list of GIPHY emojis. | | `GIPHY_EMOJI_VARIATIONS` | Emoji Variations | Tool to fetch variations for a specific emoji. Use when you have an emoji's GIF ID and want its variations. | | `GIPHY_GET_CONTENT_BY_ID` | Get Content by ID | Tool to fetch content metadata by its unique ID. Generalizes the Get GIF by ID endpoint for all content types (GIFs, Stickers, and Clips). | | `GIPHY_GET_CONTENT_BY_IDS` | Get Content by IDs | Tool to fetch metadata for multiple pieces of content (GIFs, Stickers, or Clips) by their IDs. Use when you need information on up to 100 known content IDs across any GIPHY content type. | | `GIPHY_GET_RANDOM_ID` | Giphy Get Random ID | Tool to generate a unique random ID from Giphy. Use when you need to create a unique identifier for a new user that can be used with other Giphy endpoints to personalize API responses. | | `GIPHY_RANDOM_GIF` | Giphy Random GIF | Tool to fetch a random GIF from Giphy. Use when you need a single random GIF, optionally filtered by tag or rating. Example: 'Get a random cat GIF rated G.' | | `GIPHY_RANDOM_STICKER` | Giphy Random Sticker | Tool to fetch a single random sticker. Use when you need a random sticker, optionally filtered by tag or rating. | | `GIPHY_SEARCH_CHANNELS` | GIPHY: Search Channels | Tool to search for GIPHY channels by query term. Use when you need to find channels matching a specific topic or keyword. | | `GIPHY_SEARCH_GIFS` | GIPHY: Search GIFs | Tool to search GIPHY's GIF library. Use when you need to find GIFs by keyword or phrase. | | `GIPHY_SEARCH_STICKERS` | GIPHY: Search Stickers | Tool to search GIPHY's sticker library. Use when you need to find stickers by keyword or phrase. | | `GIPHY_TAG_RANDOM` | GIPHY: Random Tag | Tool to fetch a single random tag from Giphy. Use when you need a random search term tag, optionally filtered by a query term. | | `GIPHY_TAG_RELATED` | Get Related Tags | Tool to fetch tags related to a specified tag. Use when you want to find semantically related tags for improved search suggestion. | | `GIPHY_TAG_SEARCH` | GIPHY: Tag Search | Tool to search GIPHY's tag library for autocomplete suggestions. Use when you need to generate tag suggestions for a user query. | | `GIPHY_TAG_TRENDING` | GIPHY Trending Tags | Tool to fetch the most popular search terms (tags) on GIPHY. Use when you need trending tags for content discovery. | | `GIPHY_TRANSLATE_GIF` | GIPHY Translate GIF | Tool to translate a term or phrase into a single GIF using GIPHY's special algorithm. Use when you need to convert words into the perfect GIF representation. | | `GIPHY_TRANSLATE_STICKER` | GIPHY Translate Sticker | Tool to translate a term or phrase into a single sticker using GIPHY’s translation algorithm. Use after confirming the exact phrase to visualize as a sticker. | | `GIPHY_TRENDING_GIFS` | GIPHY Trending GIFs | Tool to fetch trending GIFs from GIPHY. Use when you need the most relevant and engaging GIFs currently trending. | | `GIPHY_TRENDING_STICKERS` | Get Trending Stickers | Tool to fetch trending stickers. Use when you need the most relevant and engaging sticker GIFs currently trending. | | `GIPHY_UPLOAD_GIF` | Giphy Upload GIF | Tool to upload a GIF or video file to GIPHY. Use when you need to create a new GIPHY entry from a local file or public URL. Requires developer API key and supports optional metadata. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Giphy MCP server is an implementation of the Model Context Protocol that connects your AI agent to Giphy. It provides structured and secure access so your agent can perform Giphy 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 you begin, make sure you have: - Python 3.8/Node 16 or higher installed - A Composio account with the API key - An OpenAI API key - A Giphy account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Giphy No description provided. ### 2. Installing dependencies No description provided. ```python pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv ``` ```typescript npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv ``` ### 3. Set environment variables Create a .env file in your project root: These credentials will be used to: - Authenticate with OpenAI's GPT-5 model - Connect to Composio's Tool Router - Identify your Composio user session for Giphy access ```bash OPENAI_API_KEY=your-openai-api-key COMPOSIO_API_KEY=your-composio-api-key COMPOSIO_USER_ID=your-user-id ``` ### 4. Import modules No description provided. ```python import asyncio import os import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); ``` ### 5. Load environment variables and initialize Composio No description provided. ```python OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set in the environment") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set in the environment") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set in the environment") ``` ```typescript const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set"); if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set"); if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set"); ``` ### 6. Create a Tool Router session and build the agent function What's happening here: - We create a Composio client using your API key and configure it with the LlamaIndex provider - We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, giphy) - The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools - LlamaIndex will connect to this endpoint to dynamically discover and use the available Giphy tools. - The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent. ```python async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["giphy"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Giphy actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Giphy actions. """ return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True) ``` ```typescript async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["giphy"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description : "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Giphy actions." , llm, tools, }); return agent; } ``` ### 7. Create an interactive chat loop No description provided. ```python async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") ``` ```typescript async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } ``` ### 8. Define the main entry point What's happening here: - We're orchestrating the entire application flow - The agent gets built with proper error handling - Then we kick off the interactive chat loop so you can start talking to Giphy ```python async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err) { console.error("Failed to start agent:", err); process.exit(1); } } main(); ``` ### 9. Run the agent When prompted, authenticate and authorise your agent with Giphy, then start asking questions. ```bash python llamaindex_agent.py ``` ```typescript npx ts-node llamaindex-agent.ts ``` ## Complete Code ```python import asyncio import os import signal import dotenv from composio import Composio from composio_llamaindex import LlamaIndexProvider from llama_index.core.agent.workflow import ReActAgent from llama_index.core.workflow import Context from llama_index.llms.openai import OpenAI from llama_index.tools.mcp import BasicMCPClient, McpToolSpec dotenv.load_dotenv() OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY") COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID") if not OPENAI_API_KEY: raise ValueError("OPENAI_API_KEY is not set") if not COMPOSIO_API_KEY: raise ValueError("COMPOSIO_API_KEY is not set") if not COMPOSIO_USER_ID: raise ValueError("COMPOSIO_USER_ID is not set") async def build_agent() -> ReActAgent: composio_client = Composio( api_key=COMPOSIO_API_KEY, provider=LlamaIndexProvider(), ) session = composio_client.create( user_id=COMPOSIO_USER_ID, toolkits=["giphy"], ) mcp_url = session.mcp.url print(f"Composio MCP URL: {mcp_url}") mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY}) mcp_tool_spec = McpToolSpec(client=mcp_client) tools = await mcp_tool_spec.to_tool_list_async() llm = OpenAI(model="gpt-5") description = "An agent that uses Composio Tool Router MCP tools to perform Giphy actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Giphy actions. """ return ReActAgent( tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True, ); async def chat_loop(agent: ReActAgent) -> None: ctx = Context(agent) print("Type 'quit', 'exit', or Ctrl+C to stop.") while True: try: user_input = input("\nYou: ").strip() except (KeyboardInterrupt, EOFError): print("\nBye!") break if not user_input or user_input.lower() in {"quit", "exit"}: print("Bye!") break try: print("Agent: ", end="", flush=True) handler = agent.run(user_input, ctx=ctx) async for event in handler.stream_events(): # Stream token-by-token from LLM responses if hasattr(event, "delta") and event.delta: print(event.delta, end="", flush=True) # Show tool calls as they happen elif hasattr(event, "tool_name"): print(f"\n[Using tool: {event.tool_name}]", flush=True) # Get final response response = await handler print() # Newline after streaming except KeyboardInterrupt: print("\n[Interrupted]") continue except Exception as e: print(f"\nError: {e}") async def main() -> None: agent = await build_agent() await chat_loop(agent) if __name__ == "__main__": # Handle Ctrl+C gracefully signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0))) try: asyncio.run(main()) except KeyboardInterrupt: print("\nBye!") ``` ```typescript import "dotenv/config"; import readline from "node:readline/promises"; import { stdin as input, stdout as output } from "node:process"; import { Composio } from "@composio/core"; import { LlamaindexProvider } from "@composio/llamaindex"; import { mcp } from "@llamaindex/tools"; import { agent as createAgent } from "@llamaindex/workflow"; import { openai } from "@llamaindex/openai"; dotenv.config(); const OPENAI_API_KEY = process.env.OPENAI_API_KEY; const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY; const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID; if (!OPENAI_API_KEY) { throw new Error("OPENAI_API_KEY is not set in the environment"); } if (!COMPOSIO_API_KEY) { throw new Error("COMPOSIO_API_KEY is not set in the environment"); } if (!COMPOSIO_USER_ID) { throw new Error("COMPOSIO_USER_ID is not set in the environment"); } async function buildAgent() { console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`); console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`); const composio = new Composio({ apiKey: COMPOSIO_API_KEY, provider: new LlamaindexProvider(), }); const session = await composio.create( COMPOSIO_USER_ID!, { toolkits: ["giphy"], }, ); const mcpUrl = session.mcp.url; console.log(`Composio Tool Router MCP URL: ${mcpUrl}`); const server = mcp({ url: mcpUrl, clientName: "composio_tool_router_with_llamaindex", requestInit: { headers: { "x-api-key": COMPOSIO_API_KEY!, }, }, // verbose: true, }); const tools = await server.tools(); const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" }); const agent = createAgent({ name: "composio_tool_router_with_llamaindex", description: "An agent that uses Composio Tool Router MCP tools to perform actions.", systemPrompt: "You are a helpful assistant connected to Composio Tool Router."+ "Use the available tools to answer user queries and perform Giphy actions." , llm, tools, }); return agent; } async function chatLoop(agent: ReturnType) { const rl = readline.createInterface({ input, output }); console.log("Type 'quit' or 'exit' to stop."); while (true) { let userInput: string; try { userInput = (await rl.question("\nYou: ")).trim(); } catch { console.log("\nAgent: Bye!"); break; } if (!userInput) { continue; } const lower = userInput.toLowerCase(); if (lower === "quit" || lower === "exit") { console.log("Agent: Bye!"); break; } try { process.stdout.write("Agent: "); const stream = agent.runStream(userInput); let finalResult: any = null; for await (const event of stream) { // The event.data contains the streamed content const data: any = event.data; // Check for streaming delta content if (data?.delta) { process.stdout.write(data.delta); } // Store final result for fallback if (data?.result || data?.message) { finalResult = data; } } // If no streaming happened, show the final result if (finalResult) { const answer = finalResult.result ?? finalResult.message?.content ?? finalResult.message ?? ""; if (answer && typeof answer === "string" && !answer.includes("[object")) { process.stdout.write(answer); } } console.log(); // New line after streaming completes } catch (err: any) { console.error("\nAgent error:", err?.message ?? err); } } rl.close(); } async function main() { try { const agent = await buildAgent(); await chatLoop(agent); } catch (err: any) { console.error("Failed to start agent:", err?.message ?? err); process.exit(1); } } main(); ``` ## Conclusion You've successfully connected Giphy to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Giphy tools through an MCP endpoint - LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations - The agent becomes more capable without increasing prompt size - Async Python provides clean, efficient execution of agent workflows You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter. ## How to build Giphy MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/giphy/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/giphy/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/giphy/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/giphy/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/giphy/framework/codex) - [OpenClaw](https://composio.dev/toolkits/giphy/framework/openclaw) - [Hermes](https://composio.dev/toolkits/giphy/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/giphy/framework/cli) - [Google ADK](https://composio.dev/toolkits/giphy/framework/google-adk) - [LangChain](https://composio.dev/toolkits/giphy/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/giphy/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/giphy/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/giphy/framework/crew-ai) ## Related Toolkits - [Youtube](https://composio.dev/toolkits/youtube) - YouTube is a leading video-sharing platform for uploading, streaming, and discovering content. It empowers creators and businesses to reach global audiences and monetize their work. - [Amara](https://composio.dev/toolkits/amara) - Amara is a collaborative platform for creating and managing subtitles and captions for videos. It helps make content accessible and multilingual for global audiences. - [Cats](https://composio.dev/toolkits/cats) - Cats is an API with a huge library of cat images, breed data, and cat facts. It makes finding adorable cat photos and trivia effortless for your apps and users. - [Chatfai](https://composio.dev/toolkits/chatfai) - Chatfai is an AI platform that lets users talk to AI versions of fictional characters from books, movies, and games. It offers an engaging, interactive experience for fans to chat, roleplay, and explore creative dialogues. - [Cincopa](https://composio.dev/toolkits/cincopa) - Cincopa is a multimedia platform for uploading, managing, and customizing videos, images, and audio. It helps you deliver engaging media experiences with robust APIs and flexible integrations. - [Dungeon fighter online](https://composio.dev/toolkits/dungeon_fighter_online) - Dungeon Fighter Online (DFO) is an arcade-style, side-scrolling action RPG packed with dynamic combat and progression. Play solo or with friends to battle monsters, complete quests, and upgrade your characters. - [Elevenlabs](https://composio.dev/toolkits/elevenlabs) - Elevenlabs is an advanced AI voice generation platform for lifelike, multilingual speech synthesis. Perfect for creating natural voices for videos, apps, and business content in seconds. - [Elevenreader](https://composio.dev/toolkits/elevenreader) - Elevenreader is an AI-powered text-to-speech service by ElevenLabs that converts written content into lifelike audio. It enables fast, natural audio generation from any text. - [Epic games](https://composio.dev/toolkits/epic_games) - Epic Games is a leading video game publisher and digital storefront, known for Fortnite and Unreal Engine. It lets gamers access, manage, and purchase games all in one place. - [Fal.ai](https://composio.dev/toolkits/fal_ai) - Fal.ai is a generative media platform offering 600+ AI models for images, video, voice, and audio. Developers use Fal.ai for fast, scalable access to cutting-edge generative AI tools. - [Headout](https://composio.dev/toolkits/headout) - Headout is a global platform for booking travel experiences, tours, and entertainment. It helps users discover and secure activities at top destinations, all in one place. - [Imagekit io](https://composio.dev/toolkits/imagekit_io) - ImageKit.io is a cloud-based media management platform for image and video delivery. Instantly optimize, transform, and deliver visuals globally via a lightning-fast CDN. - [Listennotes](https://composio.dev/toolkits/listennotes) - Listennotes is a powerful podcast search engine with a massive global database. Discover, search, and curate podcasts from around the world in seconds. - [News api](https://composio.dev/toolkits/news_api) - News api is a REST API for searching and retrieving live news articles from across the web. Instantly access headlines, coverage, and breaking stories from thousands of sources. - [RAWG Video Games Database](https://composio.dev/toolkits/rawg_video_games_database) - RAWG Video Games Database is the largest video game discovery and info service. Instantly access comprehensive details, ratings, and release dates for thousands of games. - [Seat geek](https://composio.dev/toolkits/seat_geek) - SeatGeek is a live event platform offering APIs for concerts, sports, and theater data. Instantly access events, venues, and performers info for smarter ticketing and discovery. - [Shotstack](https://composio.dev/toolkits/shotstack) - Shotstack is a cloud platform for programmatically generating videos, images, and audio. Automate creative content production at scale with flexible RESTful APIs. - [Spotify](https://composio.dev/toolkits/spotify) - Spotify is a streaming service for music and podcasts with millions of tracks from artists worldwide. Enjoy personalized playlists, recommendations, and seamless listening across all your devices. - [Ticketmaster](https://composio.dev/toolkits/ticketmaster) - Ticketmaster is a global platform for event discovery, ticket sales, and live entertainment management. Get real-time access to events and streamline ticketing for fans and organizers. - [Gmail](https://composio.dev/toolkits/gmail) - Gmail is Google's email service with powerful spam protection, search, and G Suite integration. It keeps your inbox organized and makes communication fast and reliable. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Giphy MCP? With a standalone Giphy MCP server, the agents and LLMs can only access a fixed set of Giphy tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Giphy and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with LlamaIndex? Yes, you can. LlamaIndex 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 Giphy tools. ### Can I manage the permissions and scopes for Giphy while using Tool Router? Yes, absolutely. You can configure which Giphy 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 Giphy data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)