How to Use the BotGenuity MCP in LlamaIndex

Q: Can I index BotGenuity conversation histories into LlamaIndex?

Yes. You use getconversationhistory to retrieve the text logs, which LlamaIndex then parses into document nodes for your vector store.

Q: Does LlamaIndex support filtering BotGenuity tools?

Yes, you can use the allowedtools filter to limit your agent's access to specific tools like chatwithbot while blocking administrative tools.

Q: How does LlamaIndex handle BotGenuity bot configurations?

The agent calls getchatbotdetails to load the current system settings, allowing LlamaIndex to ground its responses in your actual bot configuration.

Index BotGenuity chat transcripts and lead data into searchable LlamaIndex vector stores using this MCP Server.

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Setup BotGenuity with LlamaIndex

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Index BotGenuity chats for RAG pipelines

The `get_conversation_history` tool extracts raw message logs so LlamaIndex can index them into your vector database. This turns historical customer conversations into a searchable knowledge base for your support agents. Your query engine can then search these past logs alongside static documentation. By coupling this MCP tool with `list_chatbot_conversations`, your RAG pipeline gets access to real-world customer questions and verified answers.

Search BotGenuity bot configurations with LlamaIndex

The `get_chatbot_details` tool retrieves the specific system prompts and settings of your active bots. LlamaIndex parses this data to ensure your agent knows the boundaries of each customer-facing bot. When combined with `list_chatbots`, your agent can check which bot is active before running a search query. This keeps your retrieved context aligned with the active bot's specific scope.

Extract and query customer leads

The `list_captured_leads` tool pulls contact info and user details directly into your LlamaIndex data connectors. Your agent can query this index to find specific leads who asked about pricing or technical issues. To test response accuracy, you can use `chat_with_bot` to send test queries and index the resulting answers. This lets you continuously audit your bot's knowledge base performance.

Setup guide

Set up BotGenuity 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 BotGenuity 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 BotGenuity tools.",
)
response = await agent.run("List recent BotGenuity 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 BotGenuity. 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 BotGenuity MCP in LlamaIndex

Yes. You use `get_conversation_history` to retrieve the text logs, which LlamaIndex then parses into document nodes for your vector store.

Install llama-index-tools-mcp, initialize the client, and convert the tools using McpToolSpec. You can then pass these tools directly to your LlamaIndex FunctionAgent.

Yes, you can use the allowed_tools filter to limit your agent's access to specific tools like `chat_with_bot` while blocking administrative tools.

The agent calls `get_chatbot_details` to load the current system settings, allowing LlamaIndex to ground its responses in your actual bot configuration.

Your chat histories remain within your designated LlamaIndex storage. The Vinkius sandbox executes the MCP tool calls ephemerally, ensuring no support transcripts are cached or logged externally.

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