Procore MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Procore as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Procore. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Procore?"
)
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 Procore MCP Server
Connect your Procore construction management platform to any AI agent and oversee projects, quality, and field operations through natural conversation.
LlamaIndex agents combine Procore tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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 Overview — List all active construction projects with status, addresses, timelines, and budget summaries
- RFIs & Submittals — Track Requests for Information and material approvals with assignees, due dates, and response histories
- Field Observations — Review safety and quality observations from the jobsite including priority, photos, and corrective actions
- Punch Lists — Monitor deficiencies to resolve before closeout with locations, assignees, and deadlines
- Daily Logs — Access daily logs with weather, workforce counts, equipment usage, and delay notes
- Drawings — Browse blueprints, elevations, and shop drawings with revision tracking
The Procore MCP Server exposes 8 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 Procore to LlamaIndex via MCP
Follow these steps to integrate the Procore 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 8 tools from Procore
Why Use LlamaIndex with the Procore MCP Server
LlamaIndex provides unique advantages when paired with Procore through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Procore tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Procore tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Procore, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Procore tools were called, what data was returned, and how it influenced the final answer
Procore + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Procore MCP Server delivers measurable value.
Hybrid search: combine Procore real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Procore 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 Procore for fresh data
Analytical workflows: chain Procore queries with LlamaIndex's data connectors to build multi-source analytical reports
Procore MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Procore to LlamaIndex via MCP:
get_project
Includes budget, schedule, team, and project settings. Get project details
list_daily_logs
Includes weather, workforce count, equipment, notes, and delays. List daily construction logs
list_drawings
Includes discipline, set, revision, and approval status. List project drawings
list_observations
Includes type, priority, assignee, photos, and status. List field observations
list_projects
List all construction projects
list_punch_items
Includes description, location, assignee, due date, and status. List punch list items
list_rfis
Includes subject, status, assignee, due date, and response history. List RFIs for a project
list_submittals
Includes title, spec section, status, and approver. List submittals
Example Prompts for Procore in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Procore immediately.
"Show me all my active construction projects."
"List all overdue RFIs on the Skyline Tower project."
"How many open punch items on Harbor View?"
Troubleshooting Procore MCP Server with LlamaIndex
Common issues when connecting Procore to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpProcore + LlamaIndex FAQ
Common questions about integrating Procore 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 Procore 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 Procore to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.