# How to integrate Cloudlayer MCP with LlamaIndex ```json { "title": "How to integrate Cloudlayer MCP with LlamaIndex", "toolkit": "Cloudlayer", "toolkit_slug": "cloudlayer", "framework": "LlamaIndex", "framework_slug": "llama-index", "url": "https://composio.dev/toolkits/cloudlayer/framework/llama-index", "markdown_url": "https://composio.dev/toolkits/cloudlayer/framework/llama-index.md", "updated_at": "2026-05-12T10:06:50.160Z" } ``` ## Introduction This guide walks you through connecting Cloudlayer to LlamaIndex using the Composio tool router. By the end, you'll have a working Cloudlayer agent that can generate pdf from a contract html template, convert a marketing webpage to a png image, list your most recent generated assets through natural language commands. This guide will help you understand how to give your LlamaIndex agent real control over a Cloudlayer account through Composio's Cloudlayer MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Cloudlayer with - [OpenAI Agents SDK](https://composio.dev/toolkits/cloudlayer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cloudlayer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cloudlayer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cloudlayer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cloudlayer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cloudlayer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cloudlayer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cloudlayer/framework/cli) - [Google ADK](https://composio.dev/toolkits/cloudlayer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/cloudlayer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/cloudlayer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cloudlayer/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/cloudlayer/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 Cloudlayer - Connect LlamaIndex to the Cloudlayer MCP server - Build a Cloudlayer-powered agent using LlamaIndex - Interact with Cloudlayer 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 Cloudlayer MCP server, and what's possible with it? The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Cloudlayer account. It provides structured and secure access to dynamic document and asset generation, so your agent can perform actions like converting HTML or URLs to PDFs or images, managing assets, and configuring storage on your behalf. - Automated PDF and image generation: Instantly convert HTML content or public URLs into professional PDFs and images for reporting, documentation, or sharing. - Asset management and retrieval: Let your agent fetch metadata or download links for generated assets, or list your most recent document and image creations. - Dynamic storage configuration: Seamlessly add and manage external storage buckets or containers for organizing generated files and assets. - Real-time API health monitoring: Enable your agent to check Cloudlayer API status, ensuring your integrations are always up and running. - Flexible screenshot and rendering tasks: Capture dynamic webpage screenshots as images or PDFs, with full control over conversion parameters, for advanced automation workflows. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `CLOUDLAYER_ADD_STORAGE` | Add Storage | Add a user-owned S3-compatible storage configuration for storing generated assets. This action allows Enterprise plan users to configure their own S3-compatible storage (AWS S3, DigitalOcean Spaces, Wasabi, MinIO, etc.) instead of using the built-in cloud storage included with Cloudlayer accounts. Note: User storage is only available on Enterprise plans. Standard plans will receive an 'allowed: false' response indicating the feature requires a plan upgrade. | | `CLOUDLAYER_CONVERT_HTML_TO_IMAGE` | Convert HTML to Image (V2) | Convert HTML content to an image (PNG, JPG, or WebP) using the v2 API endpoint. Renders the provided HTML string using a headless browser and returns job details with the generated image asset. Supports various rendering options including viewport configuration, transparency, auto-scroll, and custom wait conditions. | | `CLOUDLAYER_CONVERT_HTML_TO_PDF_V2` | Convert HTML to PDF (v2) | Tool to convert HTML content to PDF using CloudLayer v2 API. Use when you need to generate a PDF from raw HTML with advanced options like custom paper size, margins, headers/footers, and viewport settings. The HTML is automatically Base64 encoded before sending to the API. | | `CLOUDLAYER_CONVERT_URL_TO_PDF_GET` | Convert URL to PDF (Simple) | Tool to convert a URL to PDF using GET request. Use when you need quick PDF conversion with minimal parameters and immediate result. | | `CLOUDLAYER_DELETE_STORAGE` | Delete Storage Configuration | Tool to delete a specific user storage configuration. Use when you need to remove an external bucket configuration by its ID after confirming the ID is correct. | | `CLOUDLAYER_GET_ACCOUNT_INFO` | Get Account Info | Tool to retrieve Cloudlayer account usage, credits, and document counts. Use when monitoring account limits and subscription status. | | `CLOUDLAYER_GET_ASSET` | Get Asset | Tool to retrieve a specific asset by its ID. Use when you need to fetch metadata or download URL of an existing asset after its generation. | | `CLOUDLAYER_GET_JOB_BY_ID` | Get Job By ID | Retrieve details of a specific Cloudlayer job by its ID. Use this to check the status of an async job, get the asset download URL after completion, or view job parameters. Returns 401 if the job ID doesn't exist or doesn't belong to your account. | | `CLOUDLAYER_GET_STATUS` | Get API Status | Tool to test API reachability. Use when checking if the Cloudlayer API is available. | | `CLOUDLAYER_GET_STORAGE_BY_ID` | Get Storage Configuration by ID | Tool to retrieve a specific storage configuration by its ID. Use when you need to inspect or validate details of a user storage configuration. | | `CLOUDLAYER_LIST_ASSETS` | List Assets | List assets in your CloudLayer account with cursor-based pagination. Returns PDFs and images generated via HTML/URL conversion jobs. Use this to find asset IDs for further operations like downloading or deleting assets. | | `CLOUDLAYER_LIST_JOBS` | List Jobs | List jobs in your CloudLayer account with cursor-based pagination. Use when you need to view your recent jobs and their statuses. | | `CLOUDLAYER_LIST_STORAGE` | List Storage Configurations | Retrieves all user storage configurations (S3-compatible buckets) for the authenticated Cloudlayer account. Use this to view configured external storage destinations where generated documents can be saved. Note: User Storage is an Enterprise plan feature. Non-Enterprise accounts will receive an empty list. | | `CLOUDLAYER_TEMPLATE_TO_PDF` | Template to PDF | Generate a PDF document from an HTML/Nunjucks template with dynamic data. Provide either: - A `templateId` for predefined templates from CloudLayer's template library, OR - A base64-encoded `template` string containing custom HTML/Nunjucks markup. The `data` parameter populates template variables (e.g., {{name}}, {{items}}) with your JSON data. By default, jobs run asynchronously and return a job ID to poll for completion via get_job_by_id. | | `CLOUDLAYER_URL_TO_IMAGE_POST` | Convert URL to Image | Converts a webpage URL to an image (PNG, JPG, or WebP). Supports custom viewport settings, wait conditions, transparency, auto-scroll, and thumbnail preview generation. The API is asynchronous - use the returned job ID to poll for results. | | `CLOUDLAYER_URL_TO_PDF_POST` | Convert URL to PDF | Tool to convert a URL to PDF with full parameter support. Use when you need advanced control over paper size, margins, headers/footers, or webhook callbacks. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Cloudlayer MCP server is an implementation of the Model Context Protocol that connects your AI agent to Cloudlayer. It provides structured and secure access so your agent can perform Cloudlayer 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 Cloudlayer account and project - Basic familiarity with async Python/Typescript ### 1. Getting API Keys for OpenAI, Composio, and Cloudlayer 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 Cloudlayer 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, cloudlayer) - 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 Cloudlayer 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=["cloudlayer"], ) 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 Cloudlayer actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Cloudlayer 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: ["cloudlayer"], }, ); 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 Cloudlayer 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 Cloudlayer ```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 Cloudlayer, 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=["cloudlayer"], ) 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 Cloudlayer actions." system_prompt = """ You are a helpful assistant connected to Composio Tool Router. Use the available tools to answer user queries and perform Cloudlayer 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: ["cloudlayer"], }, ); 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 Cloudlayer 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 Cloudlayer to LlamaIndex through Composio's Tool Router MCP layer. Key takeaways: - Tool Router dynamically exposes Cloudlayer 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 Cloudlayer MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/cloudlayer/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/cloudlayer/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/cloudlayer/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/cloudlayer/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/cloudlayer/framework/codex) - [OpenClaw](https://composio.dev/toolkits/cloudlayer/framework/openclaw) - [Hermes](https://composio.dev/toolkits/cloudlayer/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/cloudlayer/framework/cli) - [Google ADK](https://composio.dev/toolkits/cloudlayer/framework/google-adk) - [LangChain](https://composio.dev/toolkits/cloudlayer/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/cloudlayer/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/cloudlayer/framework/mastra-ai) - [CrewAI](https://composio.dev/toolkits/cloudlayer/framework/crew-ai) ## Related Toolkits - [Google Drive](https://composio.dev/toolkits/googledrive) - Google Drive is a cloud storage platform for uploading, sharing, and collaborating on files. It's perfect for keeping your documents accessible and organized across devices. - [Google Docs](https://composio.dev/toolkits/googledocs) - Google Docs is a cloud-based word processor that enables document creation and real-time collaboration. Its seamless sharing and version history make team editing and content management a breeze. - [Google Super](https://composio.dev/toolkits/googlesuper) - Google Super is an all-in-one suite combining Gmail, Drive, Calendar, Sheets, Analytics, and more. It gives you a unified platform to manage your digital life, boosting productivity and organization. - [Affinda](https://composio.dev/toolkits/affinda) - Affinda is an AI-powered document processing platform that automates data extraction from resumes, invoices, and more. It streamlines document-heavy workflows by turning files into structured, actionable data. - [Agility cms](https://composio.dev/toolkits/agility_cms) - Agility CMS is a headless content management system for building and managing digital experiences across platforms. It lets teams update content quickly and deliver omnichannel experiences with ease. - [Algodocs](https://composio.dev/toolkits/algodocs) - Algodocs is an AI-powered platform that automates data extraction from business documents. It delivers fast, secure, and accurate processing without templates or manual training. - [Api2pdf](https://composio.dev/toolkits/api2pdf) - Api2Pdf is a REST API for generating PDFs from HTML, URLs, and documents using powerful engines like wkhtmltopdf and Headless Chrome. It streamlines document conversion and automation for developers and businesses. - [Aryn](https://composio.dev/toolkits/aryn) - Aryn is an AI-powered platform for parsing, extracting, and analyzing data from unstructured documents. Use it to automate document processing and unlock actionable insights from your files. - [Boldsign](https://composio.dev/toolkits/boldsign) - Boldsign is a digital eSignature platform for sending, signing, and tracking documents online. Organizations use it to automate agreements and manage legally binding workflows efficiently. - [Boloforms](https://composio.dev/toolkits/boloforms) - BoloForms is an eSignature platform built for small businesses, offering unlimited signatures, templates, and forms. It simplifies digital document signing and team collaboration at a predictable, fixed price. - [Box](https://composio.dev/toolkits/box) - Box is a cloud content management and file sharing platform for businesses. It helps teams securely store, organize, and collaborate on files from anywhere. - [Carbone](https://composio.dev/toolkits/carbone) - Carbone is a blazing-fast report generator that turns JSON data into PDFs, Word docs, spreadsheets, and more using flexible templates. It lets you automate document creation at scale with minimal code. - [Castingwords](https://composio.dev/toolkits/castingwords) - CastingWords is a transcription service specializing in human-powered, accurate transcripts via a simple API. Get seamless audio-to-text conversion for interviews, meetings, podcasts, and more. - [Cloudconvert](https://composio.dev/toolkits/cloudconvert) - CloudConvert is a powerful file conversion service supporting over 200 file formats. It streamlines converting, compressing, and managing documents, media, and more, all in one place. - [Cloudpress](https://composio.dev/toolkits/cloudpress) - Cloudpress is a content export tool for Google Docs and Notion. It automates publishing to your favorite Content Management Systems. - [Contentful graphql](https://composio.dev/toolkits/contentful_graphql) - Contentful graphql is a content delivery API that lets you access Contentful data using GraphQL queries. It gives you efficient, flexible ways to fetch and manage structured content for any digital project. - [Conversion tools](https://composio.dev/toolkits/conversion_tools) - Conversion Tools is an online service for converting documents between formats such as PDF, Word, Excel, XML, and CSV. It lets you automate complex document workflows with just a few clicks. - [Convertapi](https://composio.dev/toolkits/convertapi) - ConvertAPI is a robust file conversion service for documents, images, and spreadsheets. It streamlines programmatic format changes and lets developers automate complex workflows with a single API. - [Craftmypdf](https://composio.dev/toolkits/craftmypdf) - CraftMyPDF is a web-based service for designing and generating PDFs with templates and live data. It streamlines document creation by automating personalized PDFs at scale. - [Docmosis](https://composio.dev/toolkits/docmosis) - Docmosis generates PDF and Word documents from user-defined templates. It's perfect for merging data fields to quickly produce reports, invoices, and business letters. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Cloudlayer MCP? With a standalone Cloudlayer MCP server, the agents and LLMs can only access a fixed set of Cloudlayer tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Cloudlayer 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 Cloudlayer tools. ### Can I manage the permissions and scopes for Cloudlayer while using Tool Router? Yes, absolutely. You can configure which Cloudlayer 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 Cloudlayer data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) ยท [Composio docs](https://docs.composio.dev/llms.txt)