How to Use the Fountain MCP in LlamaIndex

Q: How do I set up Fountain with LlamaIndex?

Install llama-index-tools-mcp. Use BasicMCPClient to connect, then pass the client to McpToolSpec and generate your asynchronous tool list.

Q: Can LlamaIndex search through Fountain applicant notes?

It can. You pull the discussion history using listapplicantnotes and index it. This lets you run semantic searches across all recruiter feedback.

Q: How do I track active openings with LlamaIndex?

The agent calls listopenings and getopeningdetails. It embeds that metadata into your index so you can query which roles have been open the longest.

Index your high-volume hiring data directly into LlamaIndex for semantic search.

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Connect Fountain MCP to LlamaIndex

Create your Vinkius account to connect Fountain 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 Fountain with LlamaIndex

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Build RAG Apps on Fountain Data

The `list_applicants` tool feeds your candidate pool straight into LlamaIndex vector stores. You stop manually sorting through applications and start querying your talent pipeline using natural language. When you run `get_applicant`, the framework indexes the candidate details alongside their application history. You build a unified knowledge base where live API data merges with your internal HR documents to ground your AI in reality.

Index Interview and Funnel History

Using `list_interview_sessions`, your agent ingests the entire scheduling calendar. You can ask your application how many interviews happened last week, and it pulls the answer from the semantic index instead of hallucinating. You map the recruitment process by indexing `list_funnel_stages` and `list_funnels`. This gives your AI client total context on where candidates get stuck, allowing you to run analytical queries against historical hiring patterns.

Query Hiring Goals via MCP Server

The `list_hiring_goals` tool lets you pull active recruitment targets into your searchable index. Your LlamaIndex application knows exactly how many workers you need for the upcoming peak season. It checks those goals against the output of `list_workers` to measure progress. Because the data sits in your vector store, you can query past configurations and compare last year's fulfillment rates against current active job posts from `list_job_posts`.

Setup guide

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

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Common questions about Fountain MCP in LlamaIndex

Install `llama-index-tools-mcp`. Use BasicMCPClient to connect, then pass the client to McpToolSpec and generate your asynchronous tool list.

It can. You pull the discussion history using `list_applicant_notes` and index it. This lets you run semantic searches across all recruiter feedback.

They work natively. You pass the allowed tools directly to the FunctionAgent, and it decides when to query the live API versus checking the existing vector store.

The agent calls `list_openings` and `get_opening_details`. It embeds that metadata into your index so you can query which roles have been open the longest.

This integration touches internal recruiter notes and candidate contact details. Vinkius operates on a zero-trust architecture, meaning your endpoint token authorizes access but no data persists on our servers after the query resolves.

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