# How to integrate Docmosis MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Docmosis MCP with Vercel AI SDK v6", "toolkit": "Docmosis", "toolkit_slug": "docmosis", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/docmosis/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/docmosis/framework/ai-sdk.md", "updated_at": "2026-05-12T10:09:06.847Z" } ``` ## Introduction This guide walks you through connecting Docmosis to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Docmosis agent that can generate monthly invoice pdf for a customer, create personalized offer letters for new hires, produce event registration forms as word docs through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Docmosis account through Composio's Docmosis MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Docmosis with - [OpenAI Agents SDK](https://composio.dev/toolkits/docmosis/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/docmosis/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/docmosis/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/docmosis/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/docmosis/framework/codex) - [OpenClaw](https://composio.dev/toolkits/docmosis/framework/openclaw) - [Hermes](https://composio.dev/toolkits/docmosis/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/docmosis/framework/cli) - [Google ADK](https://composio.dev/toolkits/docmosis/framework/google-adk) - [LangChain](https://composio.dev/toolkits/docmosis/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/docmosis/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/docmosis/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/docmosis/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Docmosis integration - Using Composio's Tool Router to dynamically load and access Docmosis tools - Creating an MCP client connection using HTTP transport - Building an interactive CLI chat interface with conversation history management - Handling tool calls and results within the Vercel AI SDK framework ## What is Vercel AI SDK? The Vercel AI SDK is a TypeScript library for building AI-powered applications. It provides tools for creating agents that can use external services and maintain conversation state. Key features include: - streamText: Core function for streaming responses with real-time tool support - MCP Client: Built-in support for Model Context Protocol via @ai-sdk/mcp - Step Counting: Control multi-step tool execution with stopWhen: stepCountIs() - OpenAI Provider: Native integration with OpenAI models ## What is the Docmosis MCP server, and what's possible with it? The Docmosis MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Docmosis account. It provides structured and secure access to your document templates and generation capabilities, so your agent can perform actions like generating documents, merging data fields, exporting PDFs or Word files, and automating report creation on your behalf. - Dynamic document generation: Instantly create PDF or Word documents from pre-built templates by merging in your custom data fields. - Automated report and invoice creation: Let your agent assemble business reports, invoices, or letters using real-time input and reusable templates. - Template management and selection: Retrieve, list, and select from available templates for different document types or business needs. - Batch document processing: Generate multiple documents at once by feeding bulk data sets—perfect for automating repetitive paperwork. - Flexible file export and delivery: Export generated documents in your preferred format and deliver them to specified locations, systems, or users automatically. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DOCMOSIS_DELETE_IMAGE` | Docmosis: Delete Image(s) | Tool to delete one or more stored images. Use when you need to remove images; ensure imageName(s) are valid before use. | | `DOCMOSIS_DELETE_TEMPLATE` | Docmosis: Delete Template(s) | Tool to delete one or more templates from the environment. Use when you need to remove templates; multiple templates can be deleted in a single request. | | `DOCMOSIS_ENVIRONMENT_READY` | Docmosis Environment Ready | Tool to verify environment readiness. Use when ensuring the environment is active and within quota before rendering documents. | | `DOCMOSIS_ENVIRONMENT_SUMMARY` | Docmosis Environment Summary | Tool to retrieve environment summary. Use when you need status, plan, and quota details of your Docmosis environment after authentication. | | `DOCMOSIS_GET_API_KEY` | Docmosis: Get API Key | Tool to extract the Docmosis API access key from connection metadata. Use before other Docmosis API calls to retrieve the Bearer token from the Authorization header. | | `DOCMOSIS_GET_BATCH_UPLOAD_STATUS` | Get Batch Upload Status | Tool to check the status of a template batch upload job. Use when monitoring batch upload progress or checking if a batch upload has completed. | | `DOCMOSIS_GET_IMAGE` | Download Docmosis Images | Tool to download one or more images. Use when you need to retrieve stored image files by name. If multiple names provided, images are returned in a zip archive. | | `DOCMOSIS_GET_RENDER_QUEUE` | Get Docmosis Render Queue | Tool to get current render queue status and utilization. Use when monitoring queue capacity before scheduling rendering tasks. | | `DOCMOSIS_GET_RENDER_TAGS` | Get Render Tags | Tool to retrieve statistics on renders tagged with user-defined phrases. Returns page counts and document counts aggregated monthly. Use when reporting activity of user groups or features. | | `DOCMOSIS_GET_SAMPLE_DATA` | Get Template Sample Data | Tool to generate sample data for a Docmosis template based on its structure. Creates placeholder values that can be used for testing renders. Returns data in JSON or XML format. | | `DOCMOSIS_GET_TEMPLATE` | Download Docmosis Templates | Tool to retrieve originally uploaded templates. Use when you need to download template files by name. If multiple names provided (up to 100), templates are returned in a zip archive. | | `DOCMOSIS_GET_TEMPLATE_DETAILS` | Get Docmosis Template Details | Tool to retrieve metadata for an uploaded template. Returns name, size, MD5 hash, last modified date, and error status. Use after uploading a template to verify it was stored correctly or to check if it has errors. | | `DOCMOSIS_GET_TEMPLATE_STRUCTURE` | Get Docmosis Template Structure | Tool to retrieve a template's parsed structure: fields, repeats, conditions, images, and refs. Use after uploading a template to inspect its JSON structure. | | `DOCMOSIS_LIST_IMAGES` | Docmosis: List Images | Tool to list available stock images. Use when you need to retrieve image names optionally filtered by folder. | | `DOCMOSIS_LIST_TEMPLATES` | Docmosis: List Templates | Tool to list all templates available in the environment. Use when you need to retrieve template names, optionally filtered by folder with pagination support. | | `DOCMOSIS_PING` | Docmosis Ping | Tool to check connectivity to Docmosis Cloud services. Use when validating that the service endpoint is reachable before other operations. | | `DOCMOSIS_PING_DOCMOSIS_SERVICE` | Ping Docmosis Service | Tool to check that Docmosis Cloud services are online and at least one server is listening. Use for diagnostics and monitoring to verify service availability. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Docmosis MCP server is an implementation of the Model Context Protocol that connects your AI agent to Docmosis. It provides structured and secure access so your agent can perform Docmosis 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: - Node.js and npm installed - A Composio account with API key - An OpenAI API key ### 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 required dependencies First, install the necessary packages for your project. What you're installing: - @ai-sdk/openai: Vercel AI SDK's OpenAI provider - @ai-sdk/mcp: MCP client for Vercel AI SDK - @composio/core: Composio SDK for tool integration - ai: Core Vercel AI SDK - dotenv: Environment variable management ```bash npm install @ai-sdk/openai @ai-sdk/mcp @composio/core ai dotenv ``` ### 3. Set up environment variables Create a .env file in your project root. What's needed: - OPENAI_API_KEY: Your OpenAI API key for GPT model access - COMPOSIO_API_KEY: Your Composio API key for tool access - COMPOSIO_USER_ID: A unique identifier for the user session ```bash OPENAI_API_KEY=your_openai_api_key_here COMPOSIO_API_KEY=your_composio_api_key_here COMPOSIO_USER_ID=your_user_id_here ``` ### 4. Import required modules and validate environment What's happening: - We're importing all necessary libraries including Vercel AI SDK's OpenAI provider and Composio - The dotenv/config import automatically loads environment variables - The MCP client import enables connection to Composio's tool server ```typescript import "dotenv/config"; import { openai } from "@ai-sdk/openai"; import { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) 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, }); ``` ### 5. Create Tool Router session and initialize MCP client What's happening: - We're creating a Tool Router session that gives your agent access to Docmosis tools - The create method takes the user ID and specifies which toolkits should be available - The returned mcp object contains the URL and authentication headers needed to connect to the MCP server - This session provides access to all Docmosis-related tools through the MCP protocol ```typescript async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["docmosis"], }); const mcpUrl = session.mcp.url; ``` ### 6. Connect to MCP server and retrieve tools What's happening: - We're creating an MCP client that connects to our Composio Tool Router session via HTTP - The mcp.url provides the endpoint, and mcp.headers contains authentication credentials - The type: "http" is important - Composio requires HTTP transport - tools() retrieves all available Docmosis tools that the agent can use ```typescript const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); ``` ### 7. Initialize conversation and CLI interface What's happening: - We initialize an empty messages array to maintain conversation history - A readline interface is created to accept user input from the command line - Instructions are displayed to guide the user on how to interact with the agent ```typescript let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to docmosis, like summarize my last 5 emails, send an email, etc... :)))\n", ); const rl = readline.createInterface({ input: process.stdin, output: process.stdout, prompt: "> ", }); rl.prompt(); ``` ### 8. Handle user input and stream responses with real-time tool feedback What's happening: - We use streamText instead of generateText to stream responses in real-time - toolChoice: "auto" allows the model to decide when to use Docmosis tools - stopWhen: stepCountIs(10) allows up to 10 steps for complex multi-tool operations - onStepFinish callback displays which tools are being used in real-time - We iterate through the text stream to create a typewriter effect as the agent responds - The complete response is added to conversation history to maintain context - Errors are caught and displayed with helpful retry suggestions ```typescript 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({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); 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 { Composio } from "@composio/core"; import * as readline from "readline"; import { streamText, type ModelMessage, stepCountIs } from "ai"; import { createMCPClient } from "@ai-sdk/mcp"; const composioAPIKey = process.env.COMPOSIO_API_KEY; const composioUserID = process.env.COMPOSIO_USER_ID; if (!process.env.OPENAI_API_KEY) 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, }); async function main() { // Create a tool router session for the user const session = await composio.create(composioUserID!, { toolkits: ["docmosis"], }); const mcpUrl = session.mcp.url; const mcpClient = await createMCPClient({ transport: { type: "http", url: mcpUrl, headers: session.mcp.headers, // Authentication headers for the Composio MCP server }, }); const tools = await mcpClient.tools(); let messages: ModelMessage[] = []; console.log("Chat started! Type 'exit' or 'quit' to end the conversation.\n"); console.log( "Ask any questions related to docmosis, like summarize my last 5 emails, send an email, etc... :)))\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({ role: "user", content: trimmedInput }); console.log("\nAgent is thinking...\n"); try { const stream = streamText({ model: openai("gpt-5"), messages, tools, toolChoice: "auto", stopWhen: stepCountIs(10), onStepFinish: (step) => { for (const toolCall of step.toolCalls) { console.log(`[Using tool: ${toolCall.toolName}]`); } if (step.toolCalls.length > 0) { console.log(""); // Add space after tool calls } }, }); for await (const chunk of stream.textStream) { process.stdout.write(chunk); } console.log("\n\n---\n"); // Get final result for message history const response = await stream.response; if (response?.messages?.length) { messages.push(...response.messages); } } catch (error) { console.error("\nAn error occurred while talking to the agent:"); console.error(error); console.log( "\nYou can try again or restart the app if it keeps happening.\n", ); } finally { rl.prompt(); } }); rl.on("close", async () => { await mcpClient.close(); console.log("\n👋 Session ended."); process.exit(0); }); } main().catch((err) => { console.error("Fatal error:", err); process.exit(1); }); ``` ## Conclusion You've successfully built a Docmosis agent using the Vercel AI SDK with streaming capabilities! This implementation provides a powerful foundation for building AI applications with natural language interfaces and real-time feedback. Key features of this implementation: - Real-time streaming responses for a better user experience with typewriter effect - Live tool execution feedback showing which tools are being used as the agent works - Dynamic tool loading through Composio's Tool Router with secure authentication - Multi-step tool execution with configurable step limits (up to 10 steps) - Comprehensive error handling for robust agent execution - Conversation history maintenance for context-aware responses You can extend this further by adding custom error handling, implementing specific business logic, or integrating additional Composio toolkits to create multi-app workflows. ## How to build Docmosis MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/docmosis/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/docmosis/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/docmosis/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/docmosis/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/docmosis/framework/codex) - [OpenClaw](https://composio.dev/toolkits/docmosis/framework/openclaw) - [Hermes](https://composio.dev/toolkits/docmosis/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/docmosis/framework/cli) - [Google ADK](https://composio.dev/toolkits/docmosis/framework/google-adk) - [LangChain](https://composio.dev/toolkits/docmosis/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/docmosis/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/docmosis/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/docmosis/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. - [Cloudlayer](https://composio.dev/toolkits/cloudlayer) - Cloudlayer is a document and asset generation service for creating PDFs and images via API or SDKs. It lets you automate high-quality doc creation, saving dev time and reducing manual work. - [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. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Docmosis MCP? With a standalone Docmosis MCP server, the agents and LLMs can only access a fixed set of Docmosis tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Docmosis and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Vercel AI SDK v6? Yes, you can. Vercel AI SDK v6 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 Docmosis tools. ### Can I manage the permissions and scopes for Docmosis while using Tool Router? Yes, absolutely. You can configure which Docmosis 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 Docmosis data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)