How to Use the Zenkit MCP in LlamaIndex

Q: Can Zenkit help answer questions about workspaces using LlamaIndex?

Yes. By indexing the output of listworkspaces and getworkspacedetails, you build a knowledge base where users can ask complex, natural language questions about your system's structure.

Q: Does the MCP Server support querying historical Zenkit data in LlamaIndex?

Yes. By logging actions like updateentry and deleteentry, you build an audit trail directly into your vector store, allowing users to query past states of the system.

Q: How do I check if Zenkit has available lists with LlamaIndex?

You call listworkspaces first, and then use that output to guide a subsequent call like listelements. This chained process provides the necessary context for indexing.

Index Zenkit data for semantic search with LlamaIndex knowledge-augmented AI.

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Connect Zenkit MCP to LlamaIndex

Create your Vinkius account to connect Zenkit 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 Zenkit with LlamaIndex

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Grounding LLM Answers in Zenkit Data

`list_entries` output becomes part of your searchable index. Instead of hallucinating, the query engine grounds its answers directly in real records pulled from Zenkit. You can ask about specific data points and get a precise, API-backed answer. If you need to check configuration details, `get_workspace_details` writes that metadata into the vector store. This means future queries about workspace rules or structures won't just be guesses; they'll be searchable knowledge.

Building RAG Apps with Zenkit MCP Server

The `list_elements` tool lets you index the defined fields within a list structure. This is crucial because it turns abstract schemas into concrete, queryable facts for your knowledge base. When an end-user asks a question like, 'What fields does the Sales List use?', LlamaIndex finds the answer using the indexed output of `list_elements`.

Tracking Changes with Zenkit in LlamaIndex

`update_entry` and `delete_entry` actions, when run through a structured agent, can be logged into your knowledge index. You're not just querying current data; you're building an archive of how the system has changed over time. This ability to track modifications gives your RAG application historical context that simple API calls miss.

Setup guide

Set up Zenkit 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 Zenkit 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 Zenkit tools.",
)
response = await agent.run("List recent Zenkit data")

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Common questions about Zenkit MCP in LlamaIndex

Zenkit's tools provide structured data (like list entries or workspace details). When these outputs are indexed, LlamaIndex can perform semantic searches on the actual API data, not just relying on document text.

Yes. By indexing the output of `list_workspaces` and `get_workspace_details`, you build a knowledge base where users can ask complex, natural language questions about your system's structure.

The primary data type is structured records and metadata. You're indexing entries, list definitions, and workspace parameters that define how your organizational data operates.

Yes. By logging actions like `update_entry` and `delete_entry`, you build an audit trail directly into your vector store, allowing users to query past states of the system.

You call `list_workspaces` first, and then use that output to guide a subsequent call like `list_elements`. This chained process provides the necessary context for indexing.

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