How to Use the Bot9 MCP in LlamaIndex

Index your Bot9 customer support data into LlamaIndex for grounded, searchable AI responses.

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Connect Bot9 MCP to LlamaIndex

Create your Vinkius account to connect Bot9 to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Bot9 with LlamaIndex

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Ground LlamaIndex in Bot9 data

Convert `get_conversation_history` outputs into searchable knowledge. LlamaIndex indexes these results so your agent queries actual interaction data instead of guessing. It turns raw logs into a reliable source of truth. Your RAG pipelines gain immediate access to past support resolutions.

Query Bot9 sources with LlamaIndex

Use `list_data_sources` to pull training material into your index. Your agent uses this content to answer questions with specific, verified information. This setup keeps your AI grounded in the data you already provide to your bots. It avoids the common trap of hallucinating support procedures.

Filter Bot9 tools in LlamaIndex

Restrict access by using the allowed_tools filter. You decide exactly which operations the agent can perform when building your index. It gives you granular control over what the agent reads and writes. You keep the agent focused on the specific support tasks you care about.

Setup guide

Set up Bot9 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 Bot9 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 Bot9 tools.",
)
response = await agent.run("List recent Bot9 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 Bot9. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Bot9 MCP in LlamaIndex

Connect via the McpToolSpec and call the tools to fetch data. Once you have the text, pass it into your vector store for indexing.

Yes, it treats tool outputs as searchable documents. This allows your agent to find answers from your training material during runtime.

It takes just a few lines of code. You initialize the client and convert the tools to a format the FunctionAgent understands.

No, the server talks to the API directly. Your machine acts only as a bridge for the data moving into your index.

Data is transmitted over encrypted channels. Only the specific conversation logs you query are pulled into your local index.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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