Kintone MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Kintone 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 Kintone. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Kintone?"
)
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 Kintone MCP Server
Connect your Kintone platform to any AI agent to automate your business operations. This MCP server enables your agent to interact with custom apps, manage data records, and query organizational metadata directly.
LlamaIndex agents combine Kintone tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Record Management — List, retrieve, add, and update records in any of your Kintone apps
- App Discovery — List all available applications and retrieve detailed configurations and field mappings
- Data Querying — Use Kintone's powerful query language to filter records based on complex criteria
- Form Inspection — Access form field settings and layouts to understand data structures
- Space Visibility — List members and participants within your Kintone collaboration spaces
The Kintone MCP Server exposes 10 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 Kintone to LlamaIndex via MCP
Follow these steps to integrate the Kintone 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 10 tools from Kintone
Why Use LlamaIndex with the Kintone MCP Server
LlamaIndex provides unique advantages when paired with Kintone through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kintone tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kintone tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kintone, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kintone tools were called, what data was returned, and how it influenced the final answer
Kintone + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kintone MCP Server delivers measurable value.
Hybrid search: combine Kintone real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kintone 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 Kintone for fresh data
Analytical workflows: chain Kintone queries with LlamaIndex's data connectors to build multi-source analytical reports
Kintone MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Kintone to LlamaIndex via MCP:
add_record
Requires a JSON object mapping field codes to values. Add a new record to an app
delete_records
Requires an array of record IDs. Delete records from an app
get_app_details
Get details for a specific app
get_app_layout
Get the field layout of an app
get_record
Get a specific record from an app
list_apps
Use this to identify App IDs for record operations. List all Kintone apps
list_form_fields
List form fields for an app
list_records
You can optionally provide a query string for filtering. List records from an app
list_space_members
List members of a Kintone space
update_record
Update an existing record
Example Prompts for Kintone in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Kintone immediately.
"List all my Kintone apps."
"Show records from app ID 10 where status is 'Pending'."
"Add a new record to app 12 with name 'Jane Doe' and role 'Designer'."
Troubleshooting Kintone MCP Server with LlamaIndex
Common issues when connecting Kintone to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKintone + LlamaIndex FAQ
Common questions about integrating Kintone 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 Kintone 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 Kintone to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.