# How to integrate Apipie ai MCP with Vercel AI SDK v6 ```json { "title": "How to integrate Apipie ai MCP with Vercel AI SDK v6", "toolkit": "Apipie ai", "toolkit_slug": "apipie_ai", "framework": "Vercel AI SDK", "framework_slug": "ai-sdk", "url": "https://composio.dev/toolkits/apipie_ai/framework/ai-sdk", "markdown_url": "https://composio.dev/toolkits/apipie_ai/framework/ai-sdk.md", "updated_at": "2026-05-12T10:01:39.030Z" } ``` ## Introduction This guide walks you through connecting Apipie ai to Vercel AI SDK v6 using the Composio tool router. By the end, you'll have a working Apipie ai agent that can show all available ai models for text tasks, delete unused vectors from your collection, list country restrictions for gpt-4 models through natural language commands. This guide will help you understand how to give your Vercel AI SDK agent real control over a Apipie ai account through Composio's Apipie ai MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Apipie ai with - [OpenAI Agents SDK](https://composio.dev/toolkits/apipie_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/apipie_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/apipie_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/apipie_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/apipie_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/apipie_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/apipie_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/apipie_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/apipie_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/apipie_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/apipie_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/apipie_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/apipie_ai/framework/crew-ai) ## TL;DR Here's what you'll learn: - How to set up and configure a Vercel AI SDK agent with Apipie ai integration - Using Composio's Tool Router to dynamically load and access Apipie ai 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 Apipie ai MCP server, and what's possible with it? The Apipie ai MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Apipie ai account. It provides structured and secure access to a wide range of AI models and vector operations, so your agent can perform actions like listing models, checking restrictions, managing vectors, and analyzing usage history on your behalf. - Model discovery and selection: Instantly fetch a comprehensive list of available AI models, filter by type or provider, and stay up-to-date with the latest model options. - Model restriction checks: Retrieve country-specific deployment restrictions, helping you ensure compliance and make informed choices before launching models in new regions. - Vector data management: Effortlessly delete entire vector collections or specific vectors, enabling your agent to keep your data storage clean and up to date. - Usage analytics and auditing: Access historical API query logs—see latency, token usage, costs, and source IPs—so you can monitor performance, manage expenses, and audit activity anytime. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `APIPIE_AI_ANONYMIZE_TEXT` | Anonymize Sensitive Text | Anonymize sensitive entities (PII) in text for data privacy and compliance. Use this tool to detect and replace personally identifiable information like names, phone numbers, locations, and other sensitive data with truncated SHA-256 hashes. Returns both the anonymized text and mappings showing what was replaced. | | `APIPIE_AI_CREATE_VECTOR_COLLECTION` | Create Vector Collection | Create a new vector collection (Pinecone-style index and namespace combined) in APIpie. Use this when you need to set up a new vector database for storing embeddings with a specific dimension. The dimension must match the embedding model you'll use (e.g., 1536 for OpenAI text-embedding-ada-002). | | `APIPIE_AI_DELETE_STATE` | Delete state | Tool to delete state settings from APIpie. Without query parameter deletes app-level state; with query parameter deletes specific user state. Use after configuring state to remove unwanted state records or reset configuration. | | `APIPIE_AI_DELETE_VECTORS` | Delete Vectors | Delete vectors from a vector collection in APIpie. Use this tool to: - Delete ALL vectors in a collection: set delete_all=True (requires credits) - Delete specific vectors: set delete_all=False and provide a list of vector IDs Note: Deleting by metadata filter is not currently supported - you must specify vector IDs. | | `APIPIE_AI_GET_DETAILED_MODELS` | Get Detailed Models | Fetch detailed information about available AI models including pricing, capabilities, and specifications. Use when you need comprehensive model data with pricing rates, token limits, modality support, and benchmark scores. | | `APIPIE_AI_GET_QUERY_HISTORY` | Get query history | Tool to retrieve historic API usage logs including latency, token counts, costs, and source IP. Use after authenticating to analyze past queries for cost management, performance monitoring, or auditing. | | `APIPIE_AI_GET_STATE` | Get state | Tool to retrieve current state settings including user preferences, memory configuration, and routing settings. Use when you need to check or audit the current configuration for an app or specific user. | | `APIPIE_AI_LIST_MODELS` | List AI Models | Fetch a list of available AI models from APIPie. Use this tool when you need: - Up-to-date model listings with filtering by type, subtype, or provider - Voice model listings (set voices=true) - Country restriction information (set restrictions=true) Returns models with pricing, latency, availability, and capability information. | | `APIPIE_AI_LIST_VECTOR_COLLECTIONS` | List Vector Collections | Tool to retrieve a list of all vector collections under your account. Use when you need to view available collections before performing vector operations like querying, upserting, or deleting vectors. | | `APIPIE_AI_PARSE_DOCUMENT` | Parse Document | Tool to parse document content and metadata using Apache Tika. Extract text and metadata from various document formats (PDF, DOCX, TXT, etc.). Use when you need to extract readable text or metadata from uploaded documents. | | `APIPIE_AI_TRANSCRIBE_AUDIO` | Transcribe audio to text | Tool to transcribe audio files to text using AI speech-to-text models like Whisper. Use when you need to convert spoken audio into written text. Supports multiple models and output formats. | | `APIPIE_AI_UPDATE_STATE` | Update State Settings | Tool to create or update state settings in APIpie, including configurations, deletions, and feature toggling at app or user levels. Use when you need to manage persistent state for AI completions, memory, routing, or other APIpie features. | | `APIPIE_AI_UPLOAD_FILE` | Upload File | Upload a file to APIPie and retrieve a temporary URL. Use when you need to upload an image file and get a shareable URL. Supports image formats (.png, .jpg, .jpeg, .svg, .gif, .bmp, .tif, .tiff, .webp) with a maximum size limit of 5MB. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Apipie ai MCP server is an implementation of the Model Context Protocol that connects your AI agent to Apipie ai. It provides structured and secure access so your agent can perform Apipie ai 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 Apipie ai 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 Apipie ai-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: ["apipie_ai"], }); 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 Apipie ai 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 apipie_ai, 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 Apipie ai 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: ["apipie_ai"], }); 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 apipie_ai, 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 Apipie ai 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 Apipie ai MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/apipie_ai/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/apipie_ai/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/apipie_ai/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/apipie_ai/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/apipie_ai/framework/codex) - [OpenClaw](https://composio.dev/toolkits/apipie_ai/framework/openclaw) - [Hermes](https://composio.dev/toolkits/apipie_ai/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/apipie_ai/framework/cli) - [Google ADK](https://composio.dev/toolkits/apipie_ai/framework/google-adk) - [LangChain](https://composio.dev/toolkits/apipie_ai/framework/langchain) - [Mastra AI](https://composio.dev/toolkits/apipie_ai/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/apipie_ai/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/apipie_ai/framework/crew-ai) ## Related Toolkits - [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed. - [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service. - [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows. - [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow. - [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows. - [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application. - [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities. - [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization. - [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications. - [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key. - [Bigml](https://composio.dev/toolkits/bigml) - BigML is a machine learning platform that lets you build, train, and deploy predictive models from your data. Its intuitive interface and robust API make machine learning accessible and efficient. - [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions. - [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences. - [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement. - [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team. - [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs. - [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code. - [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets. - [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features. - [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Apipie ai MCP? With a standalone Apipie ai MCP server, the agents and LLMs can only access a fixed set of Apipie ai tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Apipie ai 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 Apipie ai tools. ### Can I manage the permissions and scopes for Apipie ai while using Tool Router? Yes, absolutely. You can configure which Apipie ai 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 Apipie ai data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)