Chatwoot MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Chatwoot as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Chatwoot. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Chatwoot?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Chatwoot MCP Server
Connect your Chatwoot account to any AI agent and take full control of your customer support and engagement through natural conversation. Streamline how you manage chats across Web, WhatsApp, Facebook, and more.
LlamaIndex agents combine Chatwoot tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Conversation Oversight — List and retrieve details for all active and resolved conversations natively
- Live Replying — Send messages to customers in active chat sessions flawlessly
- Contact Management — List and retrieve detailed customer contact information and history securely
- Inbox Intelligence — Monitor all configured inboxes, including Web widgets and social integrations flawlessly
- Agent Tracking — List all support agents and manage team availability in real-time
- Message History — Access complete chat histories to understand customer context directly within your workspace
The Chatwoot MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Chatwoot to LlamaIndex via MCP
Follow these steps to integrate the Chatwoot MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 8 tools from Chatwoot
Why Use LlamaIndex with the Chatwoot MCP Server
LlamaIndex provides unique advantages when paired with Chatwoot through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Chatwoot tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Chatwoot tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Chatwoot, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Chatwoot tools were called, what data was returned, and how it influenced the final answer
Chatwoot + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Chatwoot MCP Server delivers measurable value.
Hybrid search: combine Chatwoot real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Chatwoot to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Chatwoot for fresh data
Analytical workflows: chain Chatwoot queries with LlamaIndex's data connectors to build multi-source analytical reports
Chatwoot MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Chatwoot to LlamaIndex via MCP:
get_chat_history
Retrieve the message history for a specific conversation
get_contact_details
Get detailed information for a specific customer contact
get_conversation_details
Get detailed information for a specific conversation
list_chatwoot_contacts
List all customer contacts
list_chatwoot_inboxes
List all configured inboxes (Web, WhatsApp, etc)
list_support_agents
List all support agents in the account
list_woot_conversations
List all conversations in the account
send_chat_message
Send a message to a customer in a specific conversation
Example Prompts for Chatwoot in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Chatwoot immediately.
"List all active conversations in Chatwoot."
"What did the customer in conversation ID 555 say last?"
"Reply to conversation 555: 'I'll look into this right now for you, Sarah.'"
Troubleshooting Chatwoot MCP Server with LlamaIndex
Common issues when connecting Chatwoot to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpChatwoot + LlamaIndex FAQ
Common questions about integrating Chatwoot MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Chatwoot with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Chatwoot to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.