How to Use the Kintone MCP in LlamaIndex

Q: How do I filter Kintone records in LlamaIndex?

Use the query parameter in the listrecords tool. This allows you to fetch a subset of your data before indexing it into your store.

Q: Can I index multiple Kintone apps at once?

You can call listapps to identify all relevant IDs. Then, iterate through those IDs to retrieve and index records from each app individually.

Index your Kintone records into LlamaIndex for semantic search and grounded responses.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Kintone MCP to LlamaIndex

Create your Vinkius account to connect Kintone 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.

GDPR Free for Subscribers

Setup Kintone with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Semantic search over Kintone data

Convert your Kintone records into a searchable knowledge base. Use `list_records` to pull your data, then pass those results into your indexing pipeline for high-speed retrieval. Your agent answers questions based on actual business records. It fetches the latest info using `get_record` to ensure your RAG application always provides current, grounded answers.

Unified Kintone and document indexing

Merge your Kintone app data with existing documents. Use `list_form_fields` to map your app structure, allowing your index to understand the relationship between different fields. This creates a single source of truth for your agent. You combine live API results with static files, giving your model a complete view of your business information.

Automated Kintone context updates

Keep your index fresh with targeted tool calls. Use `list_apps` to verify your data sources, then fetch specific record sets to update your vector store automatically. This reduces hallucinations by grounding your agent in real-time data. Your application relies on the actual field values retrieved from the server via `get_record`.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Kintone. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Kintone now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Kintone MCP in LlamaIndex

Use the llama-index-tools-mcp package. Instantiate the BasicMCPClient with your URL and convert the tools using McpToolSpec before passing them to your agent.

Yes. You can include these tools in a FunctionAgent. This allows your agent to fetch data, index it, and respond to queries dynamically.

Use the query parameter in the `list_records` tool. This allows you to fetch a subset of your data before indexing it into your store.

You can call `list_apps` to identify all relevant IDs. Then, iterate through those IDs to retrieve and index records from each app individually.

The server uses a strict token-based auth model. No sensitive information is cached permanently outside of your specified index storage.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.