Birdeye 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 Birdeye 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 Birdeye. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Birdeye?"
)
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 Birdeye MCP Server
Connect your Birdeye account to any AI agent and orchestrate your customer experience and reputation management workflows through natural conversation.
LlamaIndex agents combine Birdeye 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
- Review Management — List and retrieve detailed customer reviews and fetch review summaries by source.
- Customer Interaction — Reply to reviews directly from the agent to maintain high engagement.
- CX Automation — Trigger customer check-ins to automatically send review or survey requests.
- Survey Insights — List available surveys and retrieve customer responses for analysis.
- Contact Oversight — Manage your business contacts and retrieve detailed profile information.
- Location Tracking — Access and list all business locations managed within your account.
The Birdeye 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 Birdeye to LlamaIndex via MCP
Follow these steps to integrate the Birdeye 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 Birdeye
Why Use LlamaIndex with the Birdeye MCP Server
LlamaIndex provides unique advantages when paired with Birdeye through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Birdeye tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Birdeye tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Birdeye, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Birdeye tools were called, what data was returned, and how it influenced the final answer
Birdeye + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Birdeye MCP Server delivers measurable value.
Hybrid search: combine Birdeye real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Birdeye 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 Birdeye for fresh data
Analytical workflows: chain Birdeye queries with LlamaIndex's data connectors to build multi-source analytical reports
Birdeye MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Birdeye to LlamaIndex via MCP:
checkin_customer
Check-in a customer to trigger review/survey requests
get_business_info
Retrieve core business information
get_contact
Get specific contact details
get_review_summary
Get a summary of review counts by source
get_survey_responses
Get responses for a specific survey
list_contacts
List customer contacts
list_locations
List all business locations
list_reviews
List customer reviews
list_surveys
List all surveys
reply_to_review
Reply to a specific customer review
Example Prompts for Birdeye in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Birdeye immediately.
"List the last 5 reviews received on Birdeye."
"Check in a customer: John Doe, john@example.com."
"Show my survey responses for survey surv_123."
Troubleshooting Birdeye MCP Server with LlamaIndex
Common issues when connecting Birdeye to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpBirdeye + LlamaIndex FAQ
Common questions about integrating Birdeye 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 Birdeye 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 Birdeye to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.