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How to integrate Fibery MCP with Mastra AI

Connect Mastra AI to Fibery MCP. List all open tasks for your team, fetch details for project entity by id, and more using natural language, with authentication handled for you.

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Fibery is a collaborative work management platform for organizing projects, documents, and knowledge. It helps teams streamline workflows and centralize information in one space.

23 Tools

Introduction

This guide walks you through connecting Fibery to Mastra AI using the Composio tool router. By the end, you'll have a working Fibery agent that can create and update entities, run GraphQL queries, upload files, fetch your workspace schema, and count entities by type through natural language commands.

This guide will help you understand how to give your Mastra AI agent real control over a Fibery account through Composio's Fibery MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

For exact tool slugs and parameter tables, see the tool catalog in the Composio docs.

Also integrate Fibery with

Claude CodeClaude CoworkOpenAI Agents SDKClaude Agent SDKCodexOpenClawHermesAtomic AgentGoogle ADKLangChainVercel AI SDKLlamaIndexCrewAIPydantic AIAutoGen

TL;DR

Here's what you'll learn:
  • Set up your environment so Mastra, OpenAI, and Composio work together
  • Create a Tool Router session in Composio that exposes Fibery tools
  • Connect Mastra's MCP client to the Composio generated MCP URL
  • Fetch Fibery tool definitions and attach them as a toolset
  • Build a Mastra agent that can reason, call tools, and return structured results
  • Run an interactive CLI where you can chat with your Fibery agent

What is Mastra AI?

Mastra AI is a TypeScript framework for building AI agents with tool support. It provides a clean API for creating agents that can use external services through MCP.

Key features include:

  • MCP Client: Built-in support for Model Context Protocol servers
  • Toolsets: Organize tools into logical groups
  • Step Callbacks: Monitor and debug agent execution
  • OpenAI Integration: Works with OpenAI models via @ai-sdk/openai

What is the Fibery MCP server, and what's possible with it?

The Fibery MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Fibery account. It provides structured and secure access to your workspace data, so your agent can perform actions like querying entities, managing custom apps, running GraphQL queries, and organizing files—all with zero manual integration code.

  • Entity query and retrieval: Instantly fetch detailed information or lists of entities based on type, filters, and fields, making it easy to surface project or task data as needed.
  • Custom app and endpoint management: Let your agent list, inspect, or delete custom apps and endpoints, streamlining workspace configuration and app lifecycle management.
  • Flexible data manipulation with GraphQL: Execute custom GraphQL queries and mutations against your Fibery space to fetch, update, or manipulate structured data programmatically.
  • File and resource cleanup: Remove outdated files or entities efficiently, helping keep your workspace organized and clutter-free with automated deletions.
  • Authentication and workspace insights: Validate tokens securely and retrieve workspace or app metadata, ensuring your agent always operates with up-to-date context and permissions.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Node.js 18 or higher
  • A Composio account with an active API key
  • An OpenAI API key
  • Basic familiarity with TypeScript
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key.
  • You need credits or a connected billing setup to use the models.
  • Store the key somewhere safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Go to Settings and copy your API key.
  • This key lets your Mastra agent talk to Composio and reach Fibery through MCP.
3

Install dependencies

bash
npm install @composio/core @mastra/core @mastra/mcp @ai-sdk/openai dotenv

Install the required packages.

What's happening:

  • @composio/core is the Composio SDK for creating MCP sessions
  • @mastra/core provides the Agent class
  • @mastra/mcp is Mastra's MCP client
  • @ai-sdk/openai is the model wrapper for OpenAI
  • dotenv loads environment variables from .env
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
COMPOSIO_USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your requests to Composio
  • COMPOSIO_USER_ID tells Composio which user this session belongs to
  • OPENAI_API_KEY lets the Mastra agent call OpenAI models
5

Import libraries and validate environment

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string,
});
What's happening:
  • dotenv/config auto loads your .env so process.env.* is available
  • openai gives you a Mastra compatible model wrapper
  • Agent is the Mastra agent that will call tools and produce answers
  • MCPClient connects Mastra to your Composio MCP server
  • Composio is used to create a Tool Router session
6

Create a Tool Router session for Fibery

typescript
async function main() {
  const session = await composio.create(
    composioUserID as string,
    {
      toolkits: ["fibery"],
    },
  );

  const composioMCPUrl = session.mcp.url;
  console.log("Fibery MCP URL:", composioMCPUrl);
What's happening:
  • create spins up a short-lived MCP HTTP endpoint for this user
  • The toolkits array contains "fibery" for Fibery access
  • session.mcp.url is the MCP URL that Mastra's MCPClient will connect to
7

Configure Mastra MCP client and fetch tools

typescript
const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      nasdaq: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

console.log("Fetching MCP tools from Composio...");
const composioTools = await mcpClient.getTools();
console.log("Number of tools:", Object.keys(composioTools).length);
What's happening:
  • MCPClient takes an id for this client and a list of MCP servers
  • The headers property includes the x-api-key for authentication
  • getTools fetches the tool definitions exposed by the Fibery toolkit
8

Create the Mastra agent

typescript
const agent = new Agent({
    name: "fibery-mastra-agent",
    instructions: "You are an AI agent with Fibery tools via Composio.",
    model: "openai/gpt-5",
  });
What's happening:
  • Agent is the core Mastra agent
  • name is just an identifier for logging and debugging
  • instructions guide the agent to use tools instead of only answering in natural language
  • model uses openai("gpt-5") to configure the underlying LLM
9

Set up interactive chat interface

typescript
let messages: AiMessageType[] = [];

console.log("Chat started! Type 'exit' or 'quit' to end.\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({
    id: crypto.randomUUID(),
    role: "user",
    content: trimmedInput,
  });

  console.log("\nAgent is thinking...\n");

  try {
    const response = await agent.generate(messages, {
      toolsets: {
        fibery: composioTools,
      },
      maxSteps: 8,
    });

    const { text } = response;

    if (text && text.trim().length > 0) {
      console.log(`Agent: ${text}\n`);
        messages.push({
          id: crypto.randomUUID(),
          role: "assistant",
          content: text,
        });
      }
    } catch (error) {
      console.error("\nError:", error);
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    console.log("\nSession ended.");
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main().catch((err) => {
  console.error("Fatal error:", err);
  process.exit(1);
});
What's happening:
  • messages keeps the full conversation history in Mastra's expected format
  • agent.generate runs the agent with conversation history and Fibery toolsets
  • maxSteps limits how many tool calls the agent can take in a single run
  • onStepFinish is a hook that prints intermediate steps for debugging

Complete Code

Here's the complete code to get you started with Fibery and Mastra AI:

typescript
import "dotenv/config";
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { MCPClient } from "@mastra/mcp";
import { Composio } from "@composio/core";
import * as readline from "readline";

import type { AiMessageType } from "@mastra/core/agent";

const openaiAPIKey = process.env.OPENAI_API_KEY;
const composioAPIKey = process.env.COMPOSIO_API_KEY;
const composioUserID = process.env.COMPOSIO_USER_ID;

if (!openaiAPIKey) 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 as string });

async function main() {
  const session = await composio.create(composioUserID as string, {
    toolkits: ["fibery"],
  });

  const composioMCPUrl = session.mcp.url;

  const mcpClient = new MCPClient({
    id: composioUserID as string,
    servers: {
      fibery: {
        url: new URL(composioMCPUrl),
        requestInit: {
          headers: session.mcp.headers,
        },
      },
    },
    timeout: 30_000,
  });

  const composioTools = await mcpClient.getTools();

  const agent = new Agent({
    name: "fibery-mastra-agent",
    instructions: "You are an AI agent with Fibery tools via Composio.",
    model: "openai/gpt-5",
  });

  let messages: AiMessageType[] = [];

  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
    prompt: "> ",
  });

  rl.prompt();

  rl.on("line", async (input: string) => {
    const trimmed = input.trim();
    if (["exit", "quit"].includes(trimmed.toLowerCase())) {
      rl.close();
      return;
    }

    messages.push({ id: crypto.randomUUID(), role: "user", content: trimmed });

    const { text } = await agent.generate(messages, {
      toolsets: { fibery: composioTools },
      maxSteps: 8,
    });

    if (text) {
      console.log(`Agent: ${text}\n`);
      messages.push({ id: crypto.randomUUID(), role: "assistant", content: text });
    }

    rl.prompt();
  });

  rl.on("close", async () => {
    await mcpClient.disconnect();
    process.exit(0);
  });
}

main();

Conclusion

You've built a Mastra AI agent that can interact with Fibery through Composio's Tool Router. You can extend this further by:
  • Adding other toolkits like Gmail, Slack, or GitHub
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows
TOOLS

Supported Tools

Every Fibery action and event your agent gets out of the box.

Delete Custom App Endpoint

Delete a custom app endpoint by app and endpoint IDs.

Delete Entity

Permanently delete a Fibery entity by its UUID and type.

Delete File

Delete a file from Fibery storage using its secret identifier.

Execute GraphQL Query

Execute GraphQL queries or mutations against a Fibery workspace.

Get App Information

Retrieve application information, including version, name, authentication methods and data sources when available.

Get Custom App Endpoints

List custom endpoints for a given Fibery app, for use when selecting an endpoint to invoke.

Get Custom Apps

List Fibery workspace app records and their identifiers, titles, names and versions, subject to a configurable limit that defaults to 100.

Get File

Download a file from Fibery by its secret or ID.

Get GraphQL Schema

Retrieve a Fibery GraphQL introspection response as a string to discover available types, fields and operations before composing queries.

Get User Preferences

Retrieve UI preferences from the first user returned by a Fibery user query.

Refresh access token

Check Fibery access credentials and return token information.

Validate Fibery authentication and get access token

Request an access token using an OAuth2 password grant or check the configured Fibery authorization.

Create Entity

Tool to create a new Fibery entity.

Count Entities by Type

Request a count of entities for a specified Fibery type or database, such as users or features.

Fetch Datalist Options

Fetch a page of options for a field of a Fibery entity type for uses such as filtering or dropdowns.

Fetch Schema

Fetch the complete schema metadata for a Fibery workspace.

Exchange OAuth2 authorization code

Request an OAuth2 authorization-code exchange for a Fibery custom integration, using the authorization code and redirect URI.

Delete/Revoke Access Token

Delete/revoke an existing Fibery API access token by its ID.

Validate Fibery Workspace Credentials

Test Fibery workspace credentials with a user query, supporting workspace tokens, API keys, basic auth and OAuth2.

Validate Filter

Check a filter definition for a Fibery data source before using it in a query.

Update Entity

Update fields on a Fibery entity of a specified type, including text, numbers, states and relations.

Update User Preferences

Update UI preferences for the first Fibery user returned by a workspace query.

Upload File

Upload a file to Fibery storage for later attachment to entities or use in documents.

FRAMEWORKS

How to build Fibery MCP Agent with another framework

Claude Code logo

Claude Code

Use Fibery MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Fibery MCP with Claude Cowork

OpenAI Agents SDK logo

OpenAI Agents SDK

Use Fibery MCP with OpenAI Agents SDK

Claude Agent SDK logo

Claude Agent SDK

Use Fibery MCP with Claude Agent SDK

Codex logo

Codex

Use Fibery MCP with Codex

OpenClaw logo

OpenClaw

Use Fibery MCP with OpenClaw

Hermes logo

Hermes

Use Fibery MCP with Hermes

Atomic Agent logo

Atomic Agent

Use Fibery MCP with Atomic Agent

Google ADK logo

Google ADK

Use Fibery MCP with Google ADK

LangChain logo

LangChain

Use Fibery MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Fibery MCP with Vercel AI SDK

LlamaIndex logo

LlamaIndex

Use Fibery MCP with LlamaIndex

CrewAI logo

CrewAI

Use Fibery MCP with CrewAI

Pydantic AI logo

Pydantic AI

Use Fibery MCP with Pydantic AI

AutoGen logo

AutoGen

Use Fibery MCP with AutoGen

FAQ

Frequently asked questions

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