Mav MCP Server for LlamaIndexGive LlamaIndex instant access to 9 tools to Create Lead, Get Lead, Get Playbook, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Mav as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Mav app connector for LlamaIndex is a standout in the Human Resources category — giving your AI agent 9 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Mav. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Mav?"
)
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 Mav MCP Server
Connect your Mav AI recruiting account to any AI agent and manage candidate screening through natural conversation.
LlamaIndex agents combine Mav tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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.
What you can do
- Candidate Screening — Trigger automated AI screening conversations
- SMS Campaigns — Launch and manage outbound SMS recruiting campaigns
- Lead Management — Browse candidates and their qualification status
- Engagement Tracking — Monitor open rates, reply rates, and drop-offs
- Interview Data — Access responses and screening transcripts
The Mav MCP Server exposes 9 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.
All 9 Mav tools available for LlamaIndex
When LlamaIndex connects to Mav through Vinkius, your AI agent gets direct access to every tool listed below — spanning ai-recruiting, candidate-screening, sms-engagement, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a lead and trigger a playbook
Get details for a specific lead
Get details for a specific playbook
List recent activities/events
List all leads
List all available Mav playbooks
Manually opt-out a lead
Stop a running playbook for a lead
Update an existing lead
Connect Mav to LlamaIndex via MCP
Follow these steps to wire Mav into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Mav MCP Server
LlamaIndex provides unique advantages when paired with Mav through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Mav tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Mav tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Mav, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Mav tools were called, what data was returned, and how it influenced the final answer
Mav + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Mav MCP Server delivers measurable value.
Hybrid search: combine Mav real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Mav 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 Mav for fresh data
Analytical workflows: chain Mav queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Mav in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Mav immediately.
"Show active SMS campaigns and completion rates."
"Launch a screening campaign for the new Warehouse Staff list."
"Show screening results and transcripts for qualified candidates."
Troubleshooting Mav MCP Server with LlamaIndex
Common issues when connecting Mav to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMav + LlamaIndex FAQ
Common questions about integrating Mav MCP Server with LlamaIndex.