How to Use the Gingr MCP in LlamaIndex

Q: How do I index Gingr reservation histories into LlamaIndex?

Call listownerreservations to retrieve the booking history of your kennel. Pass this raw data to LlamaIndex's document parser, which converts the JSON into searchable nodes for your vector store.

Q: Can this LlamaIndex MCP Server search custom pet fields in Gingr?

Yes, you can use searchpetcustomfields to pull specific behavioral notes into your LlamaIndex pipeline. LlamaIndex indexes these fields, allowing your agent to run semantic searches over unstructured notes like dietary preferences or play styles.

Q: How do I find a specific Gingr owner's profile using my LlamaIndex query engine?

You use findownerbyemail or findownerbyphone to locate the correct profile. Once found, the LlamaIndex engine retrieves the full profile via getpetownerdetails to answer your query.

Q: Is my kennel occupancy data safe when indexing Gingr with LlamaIndex?

Yes, because all raw data pulled via getdigitalwhiteboard is processed within your private network. The MCP Server acts as an ephemeral bridge, ensuring that sensitive kennel schedules and pet medical records are never exposed to external third-party storage.

Ground your agent's answers in real-time kennel schedules by indexing Gingr data into LlamaIndex via this MCP Server.

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Connect Gingr MCP to LlamaIndex

Create your Vinkius account to connect Gingr to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Gingr with LlamaIndex

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Ground RAG pipelines in live Gingr reservation data.

`list_owner_reservations` fetches historical and upcoming booking data directly into your LlamaIndex document store. Instead of guessing booking patterns, your LlamaIndex agent queries this index to answer specific questions about past dog boarding stays. You convert these raw Gingr API responses into searchable vector embeddings within your local index. This prevents your LlamaIndex agent from hallucinating boarding dates, as every answer is grounded in actual database records.

Query kennel status using this LlamaIndex MCP Server.

`get_digital_whiteboard` exposes the current layout and location of every pet in your facility to your LlamaIndex query engine. The LlamaIndex engine indexes this real-time snapshot to answer immediate staff questions about run occupancy. This eliminates the lag between physical kennel moves and your LlamaIndex digital records. Your agent pulls the latest Gingr whiteboard state, indexes it, and immediately knows which dogs are in play yards.

Index custom pet profiles for semantic search.

`search_pet_custom_fields` extracts specialized notes, dietary restrictions, and medical histories directly into your LlamaIndex vector store. LlamaIndex ingests these custom pet fields, building a semantic index that your front-desk agents can query using natural language. If a staff member asks about dogs with specific medication needs, the LlamaIndex query engine searches the indexed fields. This semantic search returns precise Gingr matches without requiring the user to navigate complex database filters manually.

Setup guide

Set up Gingr MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Gingr MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Gingr tools.",
)
response = await agent.run("List recent Gingr data")

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Common questions about Gingr MCP in LlamaIndex

Call `list_owner_reservations` to retrieve the booking history of your kennel. Pass this raw data to LlamaIndex's document parser, which converts the JSON into searchable nodes for your vector store.

Yes, you can use `search_pet_custom_fields` to pull specific behavioral notes into your LlamaIndex pipeline. LlamaIndex indexes these fields, allowing your agent to run semantic searches over unstructured notes like dietary preferences or play styles.

It only caches if you configure your LlamaIndex vector store to do so. To get accurate, real-time data, configure LlamaIndex to call `get_digital_whiteboard` dynamically during the query execution phase rather than relying on stale indices.

You use `find_owner_by_email` or `find_owner_by_phone` to locate the correct profile. Once found, the LlamaIndex engine retrieves the full profile via `get_pet_owner_details` to answer your query.

Yes, because all raw data pulled via `get_digital_whiteboard` is processed within your private network. The MCP Server acts as an ephemeral bridge, ensuring that sensitive kennel schedules and pet medical records are never exposed to external third-party storage.

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