Porter PaaS MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Porter PaaS as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Porter PaaS. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Porter PaaS?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Porter PaaS MCP Server
Connect your Porter account to any AI agent and take full programmatic control over your Kubernetes infrastructure natively.
LlamaIndex agents combine Porter PaaS tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Projects & Clusters — List high-level organizational bounds, EKS/GKE clusters, and deployment zones
- Applications & Environments — Map staging/production namespaces, check active web services, and resolve container requirements
- Operations — Restart app pods gracefully or forcefully deploy specific image tags when resolving CI/CD breaks
- Helm Inspections — Check low-level Helm charts behind active components (like Postgres or Redis)
The Porter PaaS MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Porter PaaS to LlamaIndex via MCP
Follow these steps to integrate the Porter PaaS MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Porter PaaS
Why Use LlamaIndex with the Porter PaaS MCP Server
LlamaIndex provides unique advantages when paired with Porter PaaS through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Porter PaaS tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Porter PaaS tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Porter PaaS, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Porter PaaS tools were called, what data was returned, and how it influenced the final answer
Porter PaaS + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Porter PaaS MCP Server delivers measurable value.
Hybrid search: combine Porter PaaS real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Porter PaaS to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Porter PaaS for fresh data
Analytical workflows: chain Porter PaaS queries with LlamaIndex's data connectors to build multi-source analytical reports
Porter PaaS MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Porter PaaS to LlamaIndex via MCP:
deploy_app_tag
Assigns a raw docker registry digest/tag directly causing Kubernetes to perform an absolute image pull orchestrating a fresh deployment state spanning replica boundaries. Forcefully mutate the executed Docker image running internally
get_app
Includes explicit CPU metrics requested, RAM limits mapped locally to the JVM/Node instances, and internal registry image hashes resolving at runtime. Analyze architectural bindings orchestrating a specific App
get_cluster
Inspect deep cloud credentials generating a specific K8s Cluster
get_project
Perform structural extraction of metadata linked to a Porter Project
list_apps
Discovers precisely which App routing identities expose `porter.run` subdomains or linked target custom apex mappings. Inventory deployed discrete Applications mapping to a Cluster
list_clusters
Exposes crucial execution zones hosting absolute memory nodes. List underlying target cloud Kubernetes definitions bounds to Porter
list_environments
Extract logic isolation environments overlapping the Cluster
list_helm_releases
Vital for verifying if dependent third-party apps (e.g. Postgres databases or Metabase) deployed aside the primary stack succeeded during installation phases. List underlying operational Helm configurations inside a namespace
list_projects
Fetches indispensable integer `projectId` arrays coordinating everything strictly downstream inside AWS/GCP clusters. Identify base Porter PaaS organizational scopes
restart_app
Mandatory during severe connection leakage scenarios impacting native processes without modifying the fundamental code layer deployment tag. Instruct the Kubernetes API to bounce the App deployment replicas
Example Prompts for Porter PaaS in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Porter PaaS immediately.
"List all applications currently running in cluster ID 5 on the Production environment."
"The queue worker is completely hung. Please perform a forceful restart of the `async-worker` app."
"We just built a hotfix on main. Deploy the image tag `d83a1b1` strictly onto `portal-frontend`."
Troubleshooting Porter PaaS MCP Server with LlamaIndex
Common issues when connecting Porter PaaS to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPorter PaaS + LlamaIndex FAQ
Common questions about integrating Porter PaaS MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Porter PaaS with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Porter PaaS to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.