Giddyup MCP Server for LlamaIndexGive LlamaIndex instant access to 13 tools to Check Giddyup Status, Create Job, Create Lead, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Giddyup 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 App Connector for LlamaIndex
The Giddyup app connector for LlamaIndex is a standout in the Construction category — giving your AI agent 13 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Giddyup. "
"You have 13 tools available."
),
)
response = await agent.run(
"What tools are available in Giddyup?"
)
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 Giddyup MCP Server
Connect your Giddyup account to any AI agent and take full control of your roofing and construction workflows through natural conversation.
LlamaIndex agents combine Giddyup tool responses with indexed documents for comprehensive, grounded answers. Connect 13 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
- Lead Orchestration — List and manage potential customer records programmatically, including contact info and acquisition metadata
- Job Lifecycle — Monitor active roofing and construction projects and retrieve detailed technical metadata and status in real-time
- Customer Intelligence — Access your complete customer directory and retrieve detailed profiles to streamline field coordination
- Operational Visibility — Get a comprehensive overview of your construction pipeline using natural language commands
- Field Coordination — Access granular details for specific jobs to ensure your team has the latest project requirements
The Giddyup MCP Server exposes 13 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.
All 13 Giddyup tools available for LlamaIndex
When LlamaIndex connects to Giddyup through Vinkius, your AI agent gets direct access to every tool listed below — spanning job-dispatching, route-optimization, lead-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Verify connectivity
Create a job
Create a lead
Get customer details
Get job details
Get lead details
List customers
List invoices
List jobs
List leads
List technicians
Update a job
Update a lead
Connect Giddyup to LlamaIndex via MCP
Follow these steps to wire Giddyup into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Giddyup MCP Server
LlamaIndex provides unique advantages when paired with Giddyup through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Giddyup tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Giddyup tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Giddyup, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Giddyup tools were called, what data was returned, and how it influenced the final answer
Giddyup + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Giddyup MCP Server delivers measurable value.
Hybrid search: combine Giddyup real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Giddyup 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 Giddyup for fresh data
Analytical workflows: chain Giddyup queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Giddyup in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Giddyup immediately.
"List all active roofing jobs in Giddyup."
"Show me the latest 3 leads collected this week."
"Get details for job ID 'J-12345'."
Troubleshooting Giddyup MCP Server with LlamaIndex
Common issues when connecting Giddyup to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGiddyup + LlamaIndex FAQ
Common questions about integrating Giddyup MCP Server with LlamaIndex.