How to Use the GitBook MCP in OpenAI Agents SDK

Q: How do I search GitBook content using OpenAI Agents SDK?

You simply pass a query to the searchcontent tool within your agent's logic. It returns matching snippets that you can then feed into the model for analysis. This works even with large documentation spaces.

Q: Can OpenAI Agents SDK manage multiple GitBook organizations?

Yes, you use listorganizations to fetch all available accounts. From there, your agent can iterate through spaces and collections in each one. It handles the context switching for you.

Q: Does OpenAI Agents SDK support GitBook auditing?

You can build an auditor by cycling through listpages and checking the content of each page. The agent can flag stale information or missing sections based on your specific rules. It's a great way to maintain documentation quality.

Build production-grade documentation agents with GitBook and the OpenAI Agents SDK.

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Connect GitBook MCP to OpenAI Agents SDK

Create your Vinkius account to connect GitBook to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup GitBook with OpenAI Agents SDK

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Full document retrieval in OpenAI Agents SDK

The `get_page` tool grabs the raw content of any documentation page directly into your agent's context. It avoids the need for manual copy-pasting or web scraping. You can use `list_spaces` to map out the entire structure of your documentation. This gives your agent a clear view of the hierarchy before it decides which page to pull.

Search and index content

Your agent uses `search_content` to find specific technical answers across your entire GitBook space. It bypasses the need to crawl through every file manually. Once the relevant content is found, the agent uses `get_page` to verify the details. This ensures the information provided to the user is accurate and up to date.

Organization-wide visibility

The `list_organizations` tool lets your agent identify and switch between multiple GitBook accounts. It simplifies managing documentation across different teams or projects. After selecting an organization, use `list_collections` to narrow down the scope. This keeps your agent focused on the relevant documentation sets for the current task.

Setup guide

Set up GitBook MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all GitBook tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives GitBook tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate GitBook tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="GitBook Agent",
            instructions="You have access to GitBook tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GitBook. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about GitBook MCP in OpenAI Agents SDK

You simply pass a query to the `search_content` tool within your agent's logic. It returns matching snippets that you can then feed into the model for analysis. This works even with large documentation spaces.

Yes, you use `list_organizations` to fetch all available accounts. From there, your agent can iterate through spaces and collections in each one. It handles the context switching for you.

You can build an auditor by cycling through `list_pages` and checking the content of each page. The agent can flag stale information or missing sections based on your specific rules. It's a great way to maintain documentation quality.

The server uses a strict zero-trust model where all communication is ephemeral. Your endpoint token is the only thing required, and the connection stays within your local environment.

This server only reads your documentation text, page structures, and organization metadata. It does not touch your account credentials or payment information, as it operates with read-only access to your public and private spaces.

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