How to Use the Zeabur (PaaS Deployment) MCP in LlamaIndex

Q: How does Zeabur (PaaS Deployment) help LlamaIndex?

The MCP Server takes raw API calls—like running executecommand or sending emails—and makes those outputs searchable. This lets your RAG application answer questions based on actual deployment activity, not just memory.

Q: Can I build a searchable history using Zeabur (PaaS Deployment) in LlamaIndex?

Yes. You can query past sessions by indexing results from sendbatchemails or getbuildlogs. This creates an index that answers questions about previous system states.

Q: Does Zeabur (PaaS Deployment) support filtering data for LlamaIndex?

The setup supports allowedtools filters. You can restrict the tools available to your agent, ensuring only relevant deployment or email tasks contribute data to your knowledge index.

Index API results into knowledge bases with LlamaIndex using the Zeabur PaaS Deployment MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Zeabur (PaaS Deployment) MCP to LlamaIndex

Create your Vinkius account to connect Zeabur (PaaS Deployment) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Zeabur (PaaS Deployment) with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Indexing Build and Log Data

When a deployment runs, you can index its output. Use `get_build_logs` to pull verbose logs from a container run. By feeding these specific text outputs into your knowledge base, LlamaIndex can answer questions like 'Why did the last build fail?' based on actual historical data.

Querying Deployment Status

Don't just forget what happened during deployment. After running `prepare_deployment` or `deploy_template`, index the resulting status object. Your RAG application can then search across past MCP Server interactions to find answers grounded in previous API results.

Retrieving Service Files for Search

Need a specific config file from a service container? Use `download_file` and index its contents. This allows your agent to build a unified knowledge graph that includes not only documents but also the actual, current content of deployed application assets.

Setup guide

Set up Zeabur (PaaS Deployment) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zeabur (PaaS Deployment) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zeabur (PaaS Deployment) tools.",
)
response = await agent.run("List recent Zeabur (PaaS Deployment) data")

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 Zeabur. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Zeabur (PaaS Deployment) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Zeabur (PaaS Deployment) MCP in LlamaIndex

The MCP Server takes raw API calls—like running `execute_command` or sending emails—and makes those outputs searchable. This lets your RAG application answer questions based on actual deployment activity, not just memory.

The server primarily deals with deployment configuration data. This includes the source YAML templates and runtime logs needed to populate your index for later queries.

Yes. You can query past sessions by indexing results from `send_batch_emails` or `get_build_logs`. This creates an index that answers questions about previous system states.

The setup supports `allowed_tools` filters. You can restrict the tools available to your agent, ensuring only relevant deployment or email tasks contribute data to your knowledge index.

The MCP Server touches deployment configuration data. This encompasses the inputs provided for stages, templates, and any necessary email credentials.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript