Customerly MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Add Tag, Create Update Lead, Create Update User, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Customerly as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Customerly app connector for LlamaIndex is a standout in the Marketing category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Customerly. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Customerly?"
)
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 Customerly MCP Server
Connect your Customerly account to any AI agent and take full control of your customer success and support workflows through natural conversation.
LlamaIndex agents combine Customerly 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
- User & Lead Orchestration — Create and manage customer profiles programmatically, including synchronizing custom attributes and managing lifecycle status
- Conversation Intelligence — Access complete chat histories and retrieve detailed interaction metadata to provide high-fidelity context for support
- Engagement Tracking — Monitor active chat sessions and customer interactions in real-time to optimize your team's response strategy
- Audience Segmentation — Programmatically add or remove tags for users and leads to maintain a structured and personalized communication ecosystem
- Record Management — Securely delete user records or update contact identification to ensure your database remains perfectly coordinated and compliant
The Customerly 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.
All 8 Customerly tools available for LlamaIndex
When LlamaIndex connects to Customerly through Vinkius, your AI agent gets direct access to every tool listed below — spanning customerly, customer-success-api, live-chat-automation, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Provide tag name and contact identification. Add a tag to a contact
Create or update a lead
Provide email and optionally user_id, name, and attributes. Create or update a user
Delete a user
Get details of a specific conversation
List all conversations
List all users
Remove a tag from a contact
Connect Customerly to LlamaIndex via MCP
Follow these steps to wire Customerly into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Customerly MCP Server
LlamaIndex provides unique advantages when paired with Customerly through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Customerly tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Customerly tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Customerly, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Customerly tools were called, what data was returned, and how it influenced the final answer
Customerly + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Customerly MCP Server delivers measurable value.
Hybrid search: combine Customerly real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Customerly 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 Customerly for fresh data
Analytical workflows: chain Customerly queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Customerly in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Customerly immediately.
"List all active users in my Customerly account."
"Create a new lead for 'Jane Smith' at 'jane@example.com'."
"Show me the transcript for conversation ID 'conv_456'."
Troubleshooting Customerly MCP Server with LlamaIndex
Common issues when connecting Customerly to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCustomerly + LlamaIndex FAQ
Common questions about integrating Customerly MCP Server with LlamaIndex.