Beamer 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 Beamer 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 Beamer. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Beamer?"
)
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 Beamer MCP Server
Connect your Beamer account to any AI agent and streamline your product communication and user engagement workflows through natural conversation.
LlamaIndex agents combine Beamer 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
- Post Management — Create, list, update, and delete product update posts to keep your users informed.
- User Engagement — Monitor Beamer notifications and track how users interact with your updates.
- Analytics Insights — Retrieve real-time analytics data to understand the reach and impact of your announcements.
- Feedback Collection — List and inspect user feedback and reactions to your product changes.
- User Auditing — List managed users within your Beamer project for better oversight.
The Beamer 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 Beamer to LlamaIndex via MCP
Follow these steps to integrate the Beamer 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 Beamer
Why Use LlamaIndex with the Beamer MCP Server
LlamaIndex provides unique advantages when paired with Beamer through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Beamer tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Beamer tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Beamer, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Beamer tools were called, what data was returned, and how it influenced the final answer
Beamer + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Beamer MCP Server delivers measurable value.
Hybrid search: combine Beamer real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Beamer 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 Beamer for fresh data
Analytical workflows: chain Beamer queries with LlamaIndex's data connectors to build multi-source analytical reports
Beamer MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Beamer to LlamaIndex via MCP:
create_post
Create a new Beamer post
delete_post
Delete a Beamer post
get_analytics
Retrieve Beamer analytics data
get_feedback_details
Get details of specific feedback
get_post
Get details of a specific Beamer post
list_feedback
List customer feedback
list_notifications
List Beamer notifications
list_posts
List all Beamer posts
list_users
List Beamer users
update_post
Update an existing Beamer post
Example Prompts for Beamer in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Beamer immediately.
"List the last 5 posts published on Beamer."
"Create a new post titled 'Spring Update' with content 'We have improved performance by 20%.'"
"Show me the latest user feedback."
Troubleshooting Beamer MCP Server with LlamaIndex
Common issues when connecting Beamer to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBeamer + LlamaIndex FAQ
Common questions about integrating Beamer 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 Beamer 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 Beamer to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.