How to Use the AirMatch Pro MCP in LlamaIndex

Q: How do I get started with AirMatch Pro and LlamaIndex?

Install the llama-index-tools-mcp package. Then, create a BasicMCPClient with your Vinkius endpoint and pass it to the McpToolSpec to get a list of tools for your agent.

Q: Can I search through past proposals I created with AirMatch Pro using LlamaIndex?

Absolutely. As your agent uses getproposal and listproposals, LlamaIndex ingests the content. You can then perform semantic searches across all that text later on.

Q: Can I control which AirMatch Pro tools LlamaIndex has access to?

Yes. The McpToolSpec supports an allowedtools filter. You can give your agent access to only read-only tools like listjobs and getproposal for safety.

Turn your AirMatch Pro data into a searchable knowledge base with LlamaIndex. Ask questions, get answers grounded in your real work.

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Connect AirMatch Pro MCP to LlamaIndex

Create your Vinkius account to connect AirMatch Pro 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 AirMatch Pro with LlamaIndex

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Index Your Entire Pipeline

Connect LlamaIndex to AirMatch Pro and start indexing. Every time your agent calls `list_proposals` or `list_customers`, the results are automatically converted into vectors and stored in your knowledge base. This means your agent's memory grows with every action. Next month, you can ask "what were the key terms in the proposals for ACME Corp?" and get an answer based on the actual data you indexed.

Build a RAG App on Your Hiring Data

This isn't just about calling tools; it's about augmenting your agent's knowledge. Use the AirMatch Pro tools to pull live data from your account—like current jobs from `list_jobs` or candidate info from `get_lead`. LlamaIndex combines this live data with your indexed history. Your agent can now answer complex questions by looking at both past and present information, giving you much better context.

Query Your MCP Server with Natural Language

Once your data is indexed, you don't need to remember specific tool calls. Just ask a question. "Show me all active leads for the Senior Developer job" is enough for your LlamaIndex agent to figure out it needs to use `list_leads` and filter the results. It translates your questions into the right AirMatch Pro tool calls. This makes interacting with your data faster and more intuitive than manually calling APIs.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirMatch Pro. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AirMatch Pro MCP in LlamaIndex

Install the `llama-index-tools-mcp` package. Then, create a `BasicMCPClient` with your Vinkius endpoint and pass it to the `McpToolSpec` to get a list of tools for your agent.

Absolutely. As your agent uses `get_proposal` and `list_proposals`, LlamaIndex ingests the content. You can then perform semantic searches across all that text later on.

By grounding its answers in the data it indexes from the MCP server. When you ask about a job, the agent retrieves the actual job details from your vector store via `list_jobs` before answering.

Yes. The `McpToolSpec` supports an `allowed_tools` filter. You can give your agent access to only read-only tools like `list_jobs` and `get_proposal` for safety.

The MCP server provides live data to your LlamaIndex agent on-demand. That data is then indexed into *your* vector store, which you control. Vinkius never sees or stores the indexed contents of your proposals or customer lists.

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