Common Room MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Common Room 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 Common Room. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Common Room?"
)
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 Common Room MCP Server
Connect your AI assistant to Common Room, the intelligent community growth platform that helps organizations find and build relationships with community members.
LlamaIndex agents combine Common Room tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Contact Search — Find community members by email, name, or external identity across connected platforms.
- Segment Management — List all segments, view member counts, and add or remove contacts from specific cohorts.
- Activity Tracking — Retrieve activity feeds to understand engagement patterns and identify key contributors.
The Common Room MCP Server exposes 10 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 Common Room to LlamaIndex via MCP
Follow these steps to integrate the Common Room 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 10 tools from Common Room
Why Use LlamaIndex with the Common Room MCP Server
LlamaIndex provides unique advantages when paired with Common Room through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Common Room tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Common Room tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Common Room, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Common Room tools were called, what data was returned, and how it influenced the final answer
Common Room + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Common Room MCP Server delivers measurable value.
Hybrid search: combine Common Room real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Common Room 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 Common Room for fresh data
Analytical workflows: chain Common Room queries with LlamaIndex's data connectors to build multi-source analytical reports
Common Room MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Common Room to LlamaIndex via MCP:
add_contact_to_segment
Manually add a contact to a specific segment
get_contact_by_email
Retrieve detailed information about a member by their email
get_contact_tags
Get tags associated with a specific member
get_organization_details
Retrieve details of a specific organization
get_segment_status
Retrieve status and member count for a specific segment
list_activity_types
Retrieve a list of supported activity types in Common Room
list_segment_members
List contacts that belong to a specific segment
list_segments
Retrieve a list of all segments in Common Room
search_contacts
Search for contacts/members in your Common Room
search_organizations
Search for organizations in Common Room
Example Prompts for Common Room in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Common Room immediately.
"Search for the member with email 'dev@example.com'."
"Show me all segments and their member counts."
"Add 'Alex Chen' to the 'Enterprise Leads' segment."
Troubleshooting Common Room MCP Server with LlamaIndex
Common issues when connecting Common Room to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCommon Room + LlamaIndex FAQ
Common questions about integrating Common Room 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 Common Room 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 Common Room to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.