SIGN UPLOG IN
X (Twitter)LinkedInGitHubDiscordYouTube

How to integrate Fibery MCP with Pydantic AI

Connect Pydantic 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.

Get started for freeGet a demo
Fibery logoFibery
Api Key

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 Pydantic 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 Pydantic 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 SDKMastra AILlamaIndexCrewAIAutoGen

TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Fibery
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Fibery workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

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:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard 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.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Fibery
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Fibery
  • MCPServerStreamableHTTP connects to the Fibery MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Fibery
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["fibery"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Fibery tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
fibery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[fibery_mcp],
    instructions=(
        "You are a Fibery assistant. Use Fibery tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Fibery endpoint
  • The agent uses GPT-5 to interpret user commands and perform Fibery operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Fibery.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Fibery API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

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

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Fibery
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["fibery"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    fibery_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[fibery_mcp],
        instructions=(
            "You are a Fibery assistant. Use Fibery tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Fibery.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Fibery through Composio's Tool Router. With this setup, your agent can perform real Fibery actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Fibery for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
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

Mastra AI logo

Mastra AI

Use Fibery MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Fibery MCP with LlamaIndex

CrewAI logo

CrewAI

Use Fibery MCP with CrewAI

AutoGen logo

AutoGen

Use Fibery MCP with AutoGen

FAQ

Frequently asked questions

Start with Fibery.It takes 30 seconds.

Managed auth, hosted MCP servers, and every Fibery tool your agent needs.Free to start.

Start building