Magicplan 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 Magicplan 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 Magicplan. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Magicplan?"
)
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 Magicplan MCP Server
Connect your Magicplan workspace to any AI agent to automate your architectural and estimation workflows. This MCP server enables your agent to interact with floor plans, retrieve precise spatial measurements, and access detailed financial estimates directly from natural language interfaces.
LlamaIndex agents combine Magicplan 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
- Project Oversight — List all architectural projects and retrieve detailed metadata and status updates
- Spatial Intelligence — Access full floor plan spatial data including floors, rooms, and individual object placements
- Precise Measurements — Retrieve numeric statistics such as area, perimeter, and volume for any plan or specific room
- Estimation Audit — Access comprehensive financial breakdowns including labor, materials, taxes, and itemized positions
- User Management — List collaborators and manage workspace access across your architectural teams
- Data Collection — Query inspection forms and survey data attached directly to your floor plans
The Magicplan 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 Magicplan to LlamaIndex via MCP
Follow these steps to integrate the Magicplan 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 Magicplan
Why Use LlamaIndex with the Magicplan MCP Server
LlamaIndex provides unique advantages when paired with Magicplan through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Magicplan tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Magicplan tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Magicplan, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Magicplan tools were called, what data was returned, and how it influenced the final answer
Magicplan + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Magicplan MCP Server delivers measurable value.
Hybrid search: combine Magicplan real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Magicplan 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 Magicplan for fresh data
Analytical workflows: chain Magicplan queries with LlamaIndex's data connectors to build multi-source analytical reports
Magicplan MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Magicplan to LlamaIndex via MCP:
get_estimate_details
Get full financial breakdown for an estimate
get_plan_form_data
Retrieve forms attached to a specific plan
get_plan_measurements
Get numeric measurements for a plan
get_project_details
Get metadata for a specific project
get_project_floor_plan
Get full spatial data for a project
get_workspace_info
Get configuration for the current workspace
list_available_forms
List all data collection forms (checklists)
list_magicplan_projects
List all floor plan projects
list_project_estimates
List all financial estimates for a project
list_workspace_users
List all users in the Magicplan workspace
Example Prompts for Magicplan in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Magicplan immediately.
"List all architectural projects in my Magicplan workspace."
"Show the floor plan measurements for project ID '123'."
"Get the financial breakdown for estimate 'est-987' in project '456'."
Troubleshooting Magicplan MCP Server with LlamaIndex
Common issues when connecting Magicplan to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMagicplan + LlamaIndex FAQ
Common questions about integrating Magicplan 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 Magicplan 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 Magicplan to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.