Fountain MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fountain 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 Fountain. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Fountain?"
)
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 Fountain MCP Server
Connect your Fountain account to any AI agent to automate your high-volume hiring and applicant lifecycle management through the Model Context Protocol (MCP). Fountain is designed specifically for frontline workforce management, allowing you to streamline every stage from sourcing to onboarding. This MCP server enables you to manage your applicant funnels, track hiring progress, and oversee worker profiles directly through natural conversation.
LlamaIndex agents combine Fountain tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Applicant Oversight — List all applicants, search by email or funnel, and fetch detailed profiles including transition history.
- Funnel & Stage Management — Access and list your hiring funnels and specific stages to understand your pipeline health.
- Hiring Goal Tracking — Monitor your progress against specific hiring targets and performance metrics.
- Opening Management — List all active job openings and fetch detailed metadata for specific positions.
- Interview Coordination — List and oversee scheduled interview sessions across your organization.
- Worker Profiles — Access metadata for individuals who have successfully completed the hiring process.
- Sourcing Insights — Monitor published job posts across various channels to optimize your recruitment reach.
The Fountain MCP Server exposes 12 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 Fountain to LlamaIndex via MCP
Follow these steps to integrate the Fountain 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 12 tools from Fountain
Why Use LlamaIndex with the Fountain MCP Server
LlamaIndex provides unique advantages when paired with Fountain through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fountain tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fountain tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fountain, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fountain tools were called, what data was returned, and how it influenced the final answer
Fountain + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fountain MCP Server delivers measurable value.
Hybrid search: combine Fountain real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fountain 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 Fountain for fresh data
Analytical workflows: chain Fountain queries with LlamaIndex's data connectors to build multi-source analytical reports
Fountain MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Fountain to LlamaIndex via MCP:
get_account_details
Get organization attributes
get_applicant
Get applicant details
get_opening_details
Get opening metadata
list_applicant_notes
Get applicant discussion
list_applicants
List job applicants
list_funnel_stages
List stages in a funnel
list_funnels
List hiring funnels
list_hiring_goals
List hiring targets
list_interview_sessions
List scheduled interviews
list_job_posts
List published job posts
list_openings
List active job openings
list_workers
List hired workers
Example Prompts for Fountain in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Fountain immediately.
"List all active job openings in Fountain."
"Show me the last 10 applicants for the 'Delivery' funnel."
"Get the hiring goals summary for this quarter."
Troubleshooting Fountain MCP Server with LlamaIndex
Common issues when connecting Fountain to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFountain + LlamaIndex FAQ
Common questions about integrating Fountain 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 Fountain 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 Fountain to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.