How to Use the Mirror.xyz (Web3 Publishing Platform) MCP in OpenAI Agents SDK

Q: Can my OpenAI Agents SDK setup discover Mirror.xyz (Web3 Publishing Platform) tools automatically?

Yes, the SDK auto-discovers both getentries and getentry at runtime. Enable cacheToolsList=True in your connection parameters to avoid redundant network roundtrips during agent initialization.

Feed Mirror.xyz publication data directly to your OpenAI Agents SDK workflows with strict safety guardrails.

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Connect Mirror.xyz (Web3 Publishing Platform) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Mirror.xyz (Web3 Publishing Platform) 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 Mirror.xyz (Web3 Publishing Platform) with OpenAI Agents SDK

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Read Mirror.xyz publications directly from OpenAI Agents SDK

The `get_entries` tool fetches a clean list of publication digests for any ENS domain you feed it. Your OpenAI agent parses the returned JSON list, identifying specific publication digests without manual scraping or custom Ethereum indexers. This integration operates within your agent's active execution loop. The OpenAI dashboard traces every single call, letting you monitor API latency and token consumption while your agent processes Web3 content feeds.

Fetch full Arweave text on demand

The `get_entry` tool retrieves the complete markdown content of any post using its unique Arweave transaction digest. Your python-based agents use this MCP Server to analyze decentralized essays, summarize research, or cross-post to other channels. Built-in guardrails validate the digest parameter before the agent executes the call. You define the boundary rules, ensuring the agent never requests arbitrary external URLs or executes unapproved network requests outside the Mirror platform.

Multi-agent handoffs for editorial pipelines

Specialized agents coordinate using this MCP Server to run multi-step Web3 content pipelines. One agent scans an ENS feed using `get_entries`, then hands the relevant digests to a secondary analyst agent. The analyst agent pulls the full markdown using `get_entry` to perform semantic tagging. Because the server runs in a secure V8 sandbox, your production pipeline remains isolated from malicious markdown injection.

Setup guide

Set up Mirror.xyz (Web3 Publishing Platform) 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 Mirror.xyz (Web3 Publishing Platform) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Mirror.xyz (Web3 Publishing Platform) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Mirror.xyz (Web3 Publishing Platform) 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="Mirror.xyz (Web3 Publishing Platform) Agent",
            instructions="You have access to Mirror.xyz (Web3 Publishing Platform) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mirror.xyz. 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 Mirror.xyz (Web3 Publishing Platform) MCP in OpenAI Agents SDK

Install openai-agents first. Initialize the server using MCPServerStreamableHttp with your Vinkius endpoint URL. Pass this instance in the mcp_servers list when instantiating your agent.

Yes, the SDK auto-discovers both get_entries and get_entry at runtime. Enable cacheToolsList=True in your connection parameters to avoid redundant network roundtrips during agent initialization.

The server delivers structured JSON payloads directly to your agent. While the raw Arweave markdown is retrieved in a single payload, your agent can stream its subsequent analysis or summary to your end users.

Vinkius manages the underlying rate limiting for the GraphQL and Arweave gateways. Your Python code should handle standard HTTP 429 exceptions if your agent triggers rapid, sequential loops.

This MCP Server only handles public blockchain data, specifically ENS domains and Arweave transaction digests. No private keys, wallet credentials, or draft drafts are ever processed or exposed.

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