How to Use the Chaport MCP in LlamaIndex

Q: How do I load Chaport MCP Server tools into LlamaIndex?

Run pip install llama-index-tools-mcp and set up a BasicMCPClient. Pass that client to McpToolSpec and await the async tool list to feed your FunctionAgent.

Q: Does LlamaIndex allow sending messages back to Chaport?

It does. If you include sendagentmessage in your allowed tools filter, your agent can push replies directly to the active visitor session.

Q: How do I restrict which Chaport tools my agent sees?

Use the allowedtools parameter when configuring your tool spec. This prevents the agent from calling administrative endpoints if it only needs read access.

Turn Chaport live chats into searchable LlamaIndex knowledge bases using this managed MCP Server.

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Connect Chaport MCP to LlamaIndex

Create your Vinkius account to connect Chaport 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 Chaport with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Chaport MCP Server chat transcripts

Customer support interactions hold massive amounts of undocumented product knowledge. Your LlamaIndex application can pull full conversation logs using `get_chat_history` and immediately chunk that text for your vector store. Instead of guessing what users struggle with, your RAG pipeline grounds its answers in real support data. You query the index and get responses backed by actual resolved tickets.

Build unified visitor profiles

Sales teams need context before they jump into a live chat. By running `get_visitor_details` alongside `get_visitor_last_chat`, the agent gathers the user's entire footprint on your site. LlamaIndex takes those raw API outputs and synthesizes them with your internal CRM documents. The resulting index gives your human operators a complete, searchable dossier on whoever they are talking to.

Query live operator status

Static routing rules fail when team members step away. You can give your FunctionAgent access to `list_online_agents` and `list_chaport_operators` through the `McpToolSpec` wrapper. The system queries this real-time availability data before deciding how to handle an incoming request. If no humans are around, it falls back to your indexed knowledge base to try and resolve the issue automatically.

Setup guide

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

Run `pip install llama-index-tools-mcp` and set up a `BasicMCPClient`. Pass that client to `McpToolSpec` and await the async tool list to feed your FunctionAgent.

That is the primary use case. You use the tools to extract chat histories, index them into your vector database, and then run semantic queries against the transcripts.

It does. If you include `send_agent_message` in your allowed tools filter, your agent can push replies directly to the active visitor session.

Use the `allowed_tools` parameter when configuring your tool spec. This prevents the agent from calling administrative endpoints if it only needs read access.

The V8 Isolate sandbox ensures zero-trust execution for every request. Sensitive visitor profiles and live message payloads pass through temporary memory and are destroyed immediately after LlamaIndex receives the API response.

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