Portable.io MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Portable.io 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 Portable.io. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Portable.io?"
)
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 Portable.io MCP Server
Connect your Portable.io account to your favorite AI agent and take orchestrate your data pipelines through natural language.
LlamaIndex agents combine Portable.io tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Data Flows — List configured integration flows and view complex mapping details
- Sync Runs — Monitor execution history, track successful row counts, and spot failure logs
- Destinations & Connectors — Retrieve all supported SaaS extractors and targeted data warehouses (like Snowflake or BigQuery)
- Account Status — Check your workspace bounds and execution limits instantly
The Portable.io MCP Server exposes 6 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 Portable.io to LlamaIndex via MCP
Follow these steps to integrate the Portable.io 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 6 tools from Portable.io
Why Use LlamaIndex with the Portable.io MCP Server
LlamaIndex provides unique advantages when paired with Portable.io through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Portable.io tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Portable.io tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Portable.io, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Portable.io tools were called, what data was returned, and how it influenced the final answer
Portable.io + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Portable.io MCP Server delivers measurable value.
Hybrid search: combine Portable.io real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Portable.io 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 Portable.io for fresh data
Analytical workflows: chain Portable.io queries with LlamaIndex's data connectors to build multi-source analytical reports
Portable.io MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Portable.io to LlamaIndex via MCP:
get_account
Retrieve the exact workspace and account billing details
get_flow
Get complete configuration details of a specific data flow
list_connectors
List available pre-built API data source connectors
list_destinations
g., Snowflake, BigQuery) currently authorized to receive raw data writes from active flows. List all configured data warehouse destinations
list_flows
List all integration flows configured in Portable
list_runs
List historical execution runs for a specific data flow
Example Prompts for Portable.io in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Portable.io immediately.
"List all active ETL flows running in my Portable workspace."
"Show the recent runs for flow ID 4087 and tell me if any failed."
"What destinations are currently configured to receive data?"
Troubleshooting Portable.io MCP Server with LlamaIndex
Common issues when connecting Portable.io to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPortable.io + LlamaIndex FAQ
Common questions about integrating Portable.io 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 Portable.io 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 Portable.io to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.