Assembled MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Assembled 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 MCP SERVER
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 Assembled. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Assembled?"
)
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 Assembled MCP Server
The Assembled MCP Server provides your AI agent with direct access to your workforce management (WFM) data. Optimize your support operations by monitoring agent availability, auditing schedules, and analyzing contact volume forecasts using natural language.
LlamaIndex agents combine Assembled tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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 Capabilities
- Agent & State Tracking — List all users and monitor real-time agent states to see who is online, on break, or in a meeting.
- Team & Queue Management — Audit your support organization structure by listing teams and individual support queues.
- Schedule Oversight — Retrieve detailed agent schedules for any time range to ensure proper coverage.
- Forecasting Insights — Access contact volume forecasts to prepare for upcoming support demand.
- Operational Auditing — Quickly verify account connections and organizational metadata without manual reports.
- Secure API Access — Uses your Assembled API Key for safe and authenticated communication with your WFM data.
The Assembled MCP Server exposes 7 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 Assembled to LlamaIndex via MCP
Follow these steps to integrate the Assembled 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 7 tools from Assembled
Why Use LlamaIndex with the Assembled MCP Server
LlamaIndex provides unique advantages when paired with Assembled through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Assembled tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Assembled tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Assembled, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Assembled tools were called, what data was returned, and how it influenced the final answer
Assembled + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Assembled MCP Server delivers measurable value.
Hybrid search: combine Assembled real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Assembled 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 Assembled for fresh data
Analytical workflows: chain Assembled queries with LlamaIndex's data connectors to build multi-source analytical reports
Assembled MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Assembled to LlamaIndex via MCP:
get_account_check
Verify Assembled account connection
list_agent_states
List real-time agent states
list_forecasts
List contact volume forecasts
list_queues
List all support queues
list_schedules
List agent schedules for a time range
list_teams
List all teams
list_users
List all users in Assembled
Example Prompts for Assembled in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Assembled immediately.
"List all agents currently online in Assembled."
"Show me the schedule for 'Support Team Alpha' for today."
"What is the contact volume forecast for next Monday?"
Troubleshooting Assembled MCP Server with LlamaIndex
Common issues when connecting Assembled to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAssembled + LlamaIndex FAQ
Common questions about integrating Assembled 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 Assembled 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 Assembled to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.