# Kintone Development Support MCP Server

> Use this tool when you need to streamline kintone development and customization, as it provides instant access to API specifications, field type documentation, and best practices through natural language queries. It solves problems related to API request validation and development guidance, allowing for more efficient and accurate kintone customization. By using this tool, developers can input natural language queries and receive relevant documentation and validation outputs to support their development work.

Canonical page: https://skillsregistry.net/skills/archivierterepositories-kntn-dev-mcp  
JSON: https://api.skillsregistry.net/v1/skills/archivierterepositories-kntn-dev-mcp

## Description

Enables developers to access kintone API specifications, field type documentation, and development best practices through natural language queries. Supports API request validation and provides comprehensive development guidance for kintone customizations.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** file-system
- **Updated:** 2026-05-04

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/dv46hxb87t)
- **Repository:** <https://github.com/ArchivierteRepositories/kntn-dev-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "archivierterepositories-kntn-dev-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/archivierterepositories-kntn-dev-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/archivierterepositories-kntn-dev-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt