Zenkit 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 Zenkit 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 MCP SERVER
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 Zenkit. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Zenkit?"
)
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 Zenkit MCP Server
Connect your Zenkit account to any AI agent to streamline your productivity and project management. This MCP server enables your agent to interact with workspaces, lists (collections), and data entries directly from natural language.
LlamaIndex agents combine Zenkit 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
- Workspace Oversight — List all workspaces and retrieve their constituent lists and metadata
- List Management — Query detailed configurations and field elements for any Zenkit list
- Data Operations — List, retrieve, create, and update entries (items) within your collections
- Field Discovery — Inspect list elements to understand the data structure and field types
- Content Cleanup — Delete entries and maintain your lists directly via natural language commands
The Zenkit 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 Zenkit to LlamaIndex via MCP
Follow these steps to integrate the Zenkit 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 Zenkit
Why Use LlamaIndex with the Zenkit MCP Server
LlamaIndex provides unique advantages when paired with Zenkit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zenkit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zenkit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zenkit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zenkit tools were called, what data was returned, and how it influenced the final answer
Zenkit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zenkit MCP Server delivers measurable value.
Hybrid search: combine Zenkit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zenkit 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 Zenkit for fresh data
Analytical workflows: chain Zenkit queries with LlamaIndex's data connectors to build multi-source analytical reports
Zenkit MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Zenkit to LlamaIndex via MCP:
create_entry
Requires a JSON object with field values. Create a new entry in a list
delete_entry
Delete an entry from a list
get_list_details
Get details for a specific list
get_workspace_details
Get details for a specific workspace
list_elements
List all elements (fields) defined in a list
list_entries
List all entries (items) in a list
list_workspaces
List all workspaces and their lists
update_entry
Update an existing entry
Example Prompts for Zenkit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zenkit immediately.
"List all my Zenkit workspaces and their collections."
"Show me all entries in the list with ID '98765'."
"Create a new entry in list '98765' with name 'Finish API documentation'."
Troubleshooting Zenkit MCP Server with LlamaIndex
Common issues when connecting Zenkit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZenkit + LlamaIndex FAQ
Common questions about integrating Zenkit 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 Zenkit 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 Zenkit to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.