How to Use the Liaison MCP in LlamaIndex

Q: Can LlamaIndex use Liaison custom fields for search?

Yes, by calling listcustomfields, the agent understands the schema of your data. This allows it to perform filtered searches based on the unique attributes you track in your admissions process. It makes your knowledge base much more relevant.

Index your entire admissions history into LlamaIndex for semantic search and grounded applicant insights.

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Connect Liaison MCP to LlamaIndex

Create your Vinkius account to connect Liaison 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 Liaison with LlamaIndex

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Build a searchable applicant index

Convert your raw admissions data into a queryable knowledge base by running `list_applications` and indexing the output. Your LLM performs semantic searches against this data to answer questions about specific applicant statuses. This approach grounds your agent in live information. Instead of relying on training data, the agent retrieves the actual status of an application from your Liaison account.

Query program requirements semantically

Use `list_programs` and `get_program` to populate your index with current academic requirements. Your agent then answers queries about program eligibility by searching this structured knowledge. This method eliminates guessing. The agent looks up the exact rules stored in the server, providing accurate information based on real-time API data.

Automate batch data ingestion

Retrieve bulk records via `list_batches` and `get_batch` to keep your LlamaIndex vector store updated. This keeps your agent current with the latest admissions movements without manual exports. Your agent maintains a high-fidelity view of the applicant pool. You get immediate answers to complex questions, backed by the latest batches fetched from the source.

Setup guide

Set up Liaison 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 Liaison 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 Liaison tools.",
)
response = await agent.run("List recent Liaison data")

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Common questions about Liaison MCP in LlamaIndex

You define the tools from the server and pass them to your agent. When you ask a question, the agent uses semantic search to find relevant information from the indexed applicant files. It then calls the necessary tools if more data is needed.

You re-run your ingestion pipeline to refresh the index. By calling `list_exports` or `list_applications`, you pull the latest records into your vector store. Your agent always works with the most recent data available.

Yes, by calling `list_custom_fields`, the agent understands the schema of your data. This allows it to perform filtered searches based on the unique attributes you track in your admissions process. It makes your knowledge base much more relevant.

The server provides raw data to your agent, which is then stored in your local vector database. You maintain full control over where that index lives. We ensure your student records stay within your specified security boundaries.

Your applicant names and IDs are handled via an ephemeral connection that wipes the session once your task finishes. The server doesn't keep logs of your queries or the specific applicant data you fetch. Your sensitive information remains strictly between you and the API.

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