How to Use the Endorsal Testimonials MCP in OpenAI Agents SDK

Q: How do I install the Endorsal Testimonials MCP Server in OpenAI Agents SDK?

Run pip install openai-agents. Initialize MCPServerStreamableHttp with your endpoint and pass it to your Agent constructor via the mcpservers array. Set cacheToolsList=True to speed up tool discovery.

Q: Can the agent approve reviews automatically?

Yes. The agent reads the queue with listpendingtestimonials and executes approvependingtestimonial. You control the logic that decides which reviews pass the threshold.

Q: How do I search for specific customer quotes?

Call searchtestimonialsbykeyword. The agent passes the search term and returns matching customer feedback directly into your Python application.

Connect the Endorsal Testimonials MCP Server to OpenAI Agents SDK to automate review approvals and audit social proof with full tracing.

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Connect Endorsal Testimonials MCP to OpenAI Agents SDK

Create your Vinkius account to connect Endorsal Testimonials 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 Endorsal Testimonials with OpenAI Agents SDK

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OpenAI Agents SDK Integration

The Endorsal Testimonials MCP Server feeds social proof data directly into your automated marketing workflows. Connect your agent to pull raw feedback using `list_all_testimonials` and skip the manual exports. You configure the guardrails. The agent runs `search_testimonials_by_keyword` to find relevant quotes, and the OpenAI dashboard tracks every API call for compliance.

Automate the approval queue

This server clears your moderation backlog by exposing the approval queue to your agent. Your setup checks `list_pending_testimonials` every hour to find new submissions waiting for a decision. Instead of logging into a dashboard, the agent fires `approve_pending_testimonial` automatically for high-confidence entries. You keep the human-in-the-loop for edge cases using built-in agent handoffs.

Audit widgets instantly

You can map out your active domains and widgets using this server. Run `list_account_properties` to see exactly which brands are connected to your main account. The agent follows up with `list_display_widgets` to verify what is rendering on your site. You get a complete picture of your conversion assets through a single `quick_social_proof_audit` execution.

Setup guide

Set up Endorsal Testimonials 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 Endorsal Testimonials tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Endorsal Testimonials 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="Endorsal Testimonials Agent",
            instructions="You have access to Endorsal Testimonials 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 Endorsal Testimonials. 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 Endorsal Testimonials MCP in OpenAI Agents SDK

Run `pip install openai-agents`. Initialize `MCPServerStreamableHttp` with your endpoint and pass it to your Agent constructor via the `mcp_servers` array. Set `cacheToolsList=True` to speed up tool discovery.

Yes. The agent reads the queue with `list_pending_testimonials` and executes `approve_pending_testimonial`. You control the logic that decides which reviews pass the threshold.

You get strict guardrails around public-facing actions. If the agent tries to approve a review, the SDK validates the action before hitting the Endorsal API.

Call `search_testimonials_by_keyword`. The agent passes the search term and returns matching customer feedback directly into your Python application.

This server processes public review text, star ratings, and customer names. Vinkius runs the connection inside an isolated V8 sandbox that destroys itself after the agent disconnects. No persistent storage means your customer feedback stays private.

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