How to Use the Birdeye MCP in LlamaIndex

Q: How do I load Birdeye tools into a LlamaIndex agent?

Install llama-index-tools-mcp and initialize the client with your Vinkius URL. Then, convert the MCP tools using McpToolSpec and pass them to your FunctionAgent to start running tools like listreviews.

Q: Can LlamaIndex query Birdeye review summaries semantically?

Yes, by pulling data with getreviewsummary and indexing the counts, your LlamaIndex agent can reason about overall business health. It uses this indexed data to answer complex analytical questions about your reputation.

Q: How does the LlamaIndex agent handle Birdeye business locations?

The agent uses listlocations to gather active branch details and indexes them. When a query references a specific store, LlamaIndex performs a vector search to find the correct location ID before calling checkincustomer.

Q: Can I restrict which MCP tools LlamaIndex can access on this server?

Yes, you can use the allowedtools filter during the MCP client setup. This lets you restrict the agent to read-only tools like listreviews while blocking write operations like replytoreview.

Index Birdeye reviews and survey responses into LlamaIndex vector stores to ground your support agent in real customer data.

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Connect Birdeye MCP to LlamaIndex

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Setup Birdeye with LlamaIndex

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Build a RAG index from Birdeye reviews using this MCP Server

The `list_reviews` tool pulls active customer feedback directly into your LlamaIndex data ingestion pipeline. Instead of just reading Birdeye reviews, your LlamaIndex agent indexes the text into a vector store. This lets your LlamaIndex support agent query past Birdeye reviews to find similar customer complaints. It turns raw Birdeye feedback into a searchable LlamaIndex knowledge base that prevents hallucinations.

Ground LlamaIndex queries in Birdeye survey data

The `get_survey_responses` tool retrieves raw Birdeye feedback from specific campaigns directly into your LlamaIndex query engine. Your LlamaIndex agent parses these Birdeye responses to answer questions about customer satisfaction. By combining this with `list_surveys`, your LlamaIndex agent dynamically locates the right Birdeye survey. This ensures your LlamaIndex RAG applications use live Birdeye customer sentiment.

Map customer interactions using LlamaIndex and Birdeye

The `list_contacts` tool retrieves your Birdeye customer database directly into your LlamaIndex indexer. Your LlamaIndex agent maps these Birdeye contacts against business locations from `list_locations`. When a customer reaches out, your LlamaIndex agent calls `get_contact` to pull their Birdeye history. This Birdeye context is injected straight into your LlamaIndex prompts for hyper-personalized responses.

Setup guide

Set up Birdeye MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Birdeye MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Birdeye tools.",
)
response = await agent.run("List recent Birdeye data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Birdeye. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Birdeye MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the client with your Vinkius URL. Then, convert the MCP tools using `McpToolSpec` and pass them to your `FunctionAgent` to start running tools like `list_reviews`.

Yes, by pulling data with `get_review_summary` and indexing the counts, your LlamaIndex agent can reason about overall business health. It uses this indexed data to answer complex analytical questions about your reputation.

The agent uses `list_locations` to gather active branch details and indexes them. When a query references a specific store, LlamaIndex performs a vector search to find the correct location ID before calling `checkin_customer`.

Yes, you can use the `allowed_tools` filter during the MCP client setup. This lets you restrict the agent to read-only tools like `list_reviews` while blocking write operations like `reply_to_review`.

Yes, all contact details and survey responses are processed in isolated, stateless environments. Your customer information is never cached or stored on Vinkius servers, maintaining strict data privacy.

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