How to Use the Botsonic MCP in LlamaIndex

Q: How do I load Botsonic tools into my LlamaIndex agent?

Initialize the BasicMCPClient pointing to the Vinkius endpoint, then wrap it in McpToolSpec. You convert that to a tool list and pass it to your FunctionAgent.

Q: Can LlamaIndex index the output of the Botsonic listleads tool?

Yes, it can. The agent calls listleads to get contact details, which are then parsed into document nodes and stored in your vector index for semantic querying.

Q: Can I restrict which tools the LlamaIndex agent can access?

Yes, you can use the allowedtools filter in the MCP tool spec to limit your agent's access to read-only tools like getbotanalytics.

Q: Is my Botsonic knowledge base data safe during indexing?

Yes, all knowledge URLs and documents fetched via listknowledgebase are processed in memory within a zero-trust sandbox. No training data is cached or stored permanently on the proxy.

Index live Botsonic customer conversations and lead data directly into your LlamaIndex RAG pipelines.

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Connect Botsonic MCP to LlamaIndex

Create your Vinkius account to connect Botsonic 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 Botsonic with LlamaIndex

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Index Live Support Chat History into LlamaIndex

Stop letting customer chat logs sit idle in your support dashboard. Your LlamaIndex agent runs `list_conversations` and `get_conversation` to extract real-world customer issues, turning them into queryable document nodes. By indexing these logs, your RAG pipeline can instantly search past interactions. When a customer asks a complex question, the agent searches this local index to find how human reps solved it before.

Semantic Search Over Your Botsonic Knowledge Base

Use this MCP Server to keep your retrieval pipeline aligned with what your bot actually knows. Your agent calls `list_knowledge_base` to pull the active training URLs and documents. LlamaIndex parses these resources and maps them to your vector store. If a gap is found, the agent uses `add_knowledge_url` to feed new sources directly into the bot.

Data-Grounded Bot Performance Auditing

Build automated feedback loops that evaluate your bot using real metrics. The agent pulls live performance data using `get_bot_analytics` and indexes the results. You can then run semantic queries to find out which bots are underperforming. If a bot's deflection rate drops, your agent uses `update_bot` to adjust its configuration.

Setup guide

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

Initialize the `BasicMCPClient` pointing to the Vinkius endpoint, then wrap it in `McpToolSpec`. You convert that to a tool list and pass it to your FunctionAgent.

Yes, it can. The agent calls `list_leads` to get contact details, which are then parsed into document nodes and stored in your vector index for semantic querying.

The server manages rate limits gracefully, but you can also configure your LlamaIndex agent to throttle its calls to `get_bot` and `list_bots` during heavy runs.

Yes, you can use the `allowed_tools` filter in the MCP tool spec to limit your agent's access to read-only tools like `get_bot_analytics`.

Yes, all knowledge URLs and documents fetched via `list_knowledge_base` are processed in memory within a zero-trust sandbox. No training data is cached or stored permanently on the proxy.

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