Gingr 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 Gingr 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 Gingr. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Gingr?"
)
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 Gingr MCP Server
Connect your Gingr pet care management account to any AI agent to automate your data extraction and customer support workflows through the Model Context Protocol (MCP). Gingr is the leading platform for kennel, daycare, and grooming businesses. This MCP server enables you to retrieve detailed pet owner profiles, track upcoming and past reservations, and monitor real-time facility check-ins directly through natural conversation.
LlamaIndex agents combine Gingr 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.
Key Features
- Owner & Pet Insights — Retrieve complete profile metadata for pet owners using IDs, email addresses, or phone numbers.
- Reservation Tracking — List all boarding, daycare, and grooming reservations for any owner, filtered by status (future or currently checked-in).
- Digital Whiteboard Oversight — Access 'Back of House' data to see real-time facility activity and room assignments for any location.
- Custom Data Discovery — Search across custom metadata fields for both owners and animals to find specific regional or internal attributes.
- Facility Transparency — List all business locations and facilities configured in your Gingr app.
- Read-only Security — Safely query your pet care database with a secure, read-only integration designed for data visibility.
- Real-time Synchronization — Keep your facility operations data accessible to your AI assistant without leaving your primary workspace.
The Gingr 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 Gingr to LlamaIndex via MCP
Follow these steps to integrate the Gingr 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 Gingr
Why Use LlamaIndex with the Gingr MCP Server
LlamaIndex provides unique advantages when paired with Gingr through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Gingr tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Gingr tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Gingr, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Gingr tools were called, what data was returned, and how it influenced the final answer
Gingr + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Gingr MCP Server delivers measurable value.
Hybrid search: combine Gingr real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Gingr 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 Gingr for fresh data
Analytical workflows: chain Gingr queries with LlamaIndex's data connectors to build multi-source analytical reports
Gingr MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Gingr to LlamaIndex via MCP:
find_owner_by_email
Search owner by email
find_owner_by_phone
Search owner by phone
get_digital_whiteboard
View active whiteboard
get_pet_owner_details
Get owner profile
list_active_checkins
List currently checked-in
list_business_locations
List pet care facilities
list_owner_reservations
) for a specific owner. List past/future bookings
search_owner_custom_fields
Search custom owner data
search_pet_custom_fields
Search custom pet data
verify_api_connection
Check connection
Example Prompts for Gingr in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Gingr immediately.
"Find pet owner details for 'john@email.com'."
"List all future reservations for owner ID '12345'."
"Show me the current digital whiteboard for location '1'."
Troubleshooting Gingr MCP Server with LlamaIndex
Common issues when connecting Gingr to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGingr + LlamaIndex FAQ
Common questions about integrating Gingr 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 Gingr 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 Gingr to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.