JAJ Dataverse Dev MCP
概览
作者提供的说明(英文原文)
Xrm/Power Platform/Dataverse MCP for devs, Azure CLI auth, Web API and multi-environment support
PopNx MCP 的三大支柱
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
- 用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
- 我们的基础检查i2 项中通过 2 项依据公开信息检查;尚未扫描代码
- 使用情况i非常新↓ 67/周 · ★ 0
它能帮你做什么
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
- 连接方式: 在你的电脑上运行
- 传输方式: stdio
- 分类: 开发者工具
安装
选择你使用的 AI 应用,然后复制设置。
安装之前
- 基础检查:2 项中通过 2 项
- 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
- 需要你自己填写:
DATAVERSE_ENVIRONMENTS_PATH
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"jaj-dataverse-dev": {
"command": "npx",
"args": [
"-y",
"jaj-dataverse-dev-mcp"
],
"env": {
"DATAVERSE_ENVIRONMENTS_PATH": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add jaj-dataverse-dev --env DATAVERSE_ENVIRONMENTS_PATH=your-value -- npx -y jaj-dataverse-dev-mcp运行前请先填写空着的值。
或者自己复制设置
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"jaj-dataverse-dev": {
"command": "npx",
"args": [
"-y",
"jaj-dataverse-dev-mcp"
],
"env": {
"DATAVERSE_ENVIRONMENTS_PATH": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
或者自己复制设置
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"jaj-dataverse-dev": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"jaj-dataverse-dev-mcp"
],
"env": {
"DATAVERSE_ENVIRONMENTS_PATH": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
需要你填写的值
DATAVERSE_ENVIRONMENTS_PATHenvOptional path to the Dataverse environments configuration file. Defaults to ./environments.json.
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
哪些应用可以使用它
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它能做什么
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
基础检查i
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月11日
- ✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 jjohnsen 验证,该账户拥有此仓库
- ✓ 通过
仍在维护
代码最后更新于35天前
- … 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
- – 不适用
端点有响应
它在你的电脑上运行,没有互联网端点
- – 不适用
使用 OAuth 登录
在你的电脑上运行,改用本地设置
- … 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行;我们只能读取互联网端点的工具列表
用户评价
还没有评价
与“开发者工具”中的其他服务器比较i
- 星标数多于此分类中其他 5741 个服务器的 0%
- 发布到注册中心的时间比此分类中其他 5741 个服务器的 41% 更新
- 基础检查结果等于或优于此分类中其他 5741 个服务器的 100%
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
常见问题
JAJ Dataverse Dev MCP 安全吗?
它在 2 项适用的基础检查中通过了 2 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
它需要哪些密钥或代码?
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在我的电脑上运行还是在云端?
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
它还在维护吗?
GitHub 仓库最后更新于35天前,最新版本(0.1.4)于2个月前进入注册中心。
哪些应用可以使用它?
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
有什么可以替代它?
同一分类中有:REA, Netdata, Front-End Checklist
替代选择
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Netdata
Real-time infrastructure monitoring with metrics, logs, alerts, and ML-based anomaly detection.
Front-End Checklist
Review frontend code and live pages against 386 quality-gated web development rules.
作者的 README
打开 README
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
JAJ Dataverse Dev MCP
Lightweight MCP server for day-to-day Dataverse and Power Platform development.
Connect your agents to multiple Dataverse environments using your existing Azure CLI identity. The server exposes a thin, agent-friendly layer over the Web API for data, metadata, solutions, components, troubleshooting, and development tasks.
Agent → MCP → Azure CLI identity → Dataverse Web API
Quick Start for VS Code + GitHub Copilot
Add the server to .vscode/mcp.json:
{
"servers": { "dataverse-dev": { "command": "npx", "args": [ "-y", "jaj-dataverse-dev-mcp" ] } } }
Save the file and press Start. The first start may take some time while npx downloads the package.
Sign in with Azure CLI:
az login
Create environments.json in your project root with the environments you need:
{
"environments": { "dev": { "url": "https://org8ffb6d07.crm.dynamics.com/", "allowWrite": true } } }
Open Copilot and try:
- List the available Dataverse environments
- Call whoami for the dev environment
- Show me the names and IDs of the five most recently created accounts
- List the unmanaged solutions
That's it!
Other MCP-compatible agents follow the same pattern: run jaj-dataverse-dev-mcp over stdio and provide access to your local Azure CLI session and connection configuration.
Quick Start for Claude Desktop
Add the server to claude_desktop_config.json:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json - macOS:
~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": { "dataverse-dev": { "command": "npx", "args": ["-y", "jaj-dataverse-dev-mcp"], "env": { "DATAVERSE_ENVIRONMENTS_PATH": "C:/Users/you/project/environments.json" } } } }
Save the file and fully quit and restart Claude Desktop (config changes aren't picked up by simply closing the window).
Continue from *"Sign in with Azure CLI..."* in Quick Start for VS Code + GitHub Copilot
Why this project?
There are already several Dataverse MCP implementations, including Microsoft's own tooling.
This project grew out of day-to-day development work where agents frequently needed capabilities beyond the available specialized tools. In many cases, the agent could solve the task successfully by constructing Dataverse Web API requests directly.
This MCP embraces that approach.
Instead of hiding Dataverse behind a large abstraction, it provides broad access to the Web API through a thin wrapper.
It is also designed for developers and consultants who regularly move between projects, customers, and Dataverse environments.
One MCP server can work with multiple Dataverse environments while using existing Azure CLI identity. No separate app registration, client ID, or client secret is required.
Prerequisites
- Node.js 20+
- Azure CLI installed and signed in
- Access to one or more Dataverse environment URLs
Authentication
# Authentication is based on your current Azure CLI identity:
az login
Useful variants:
az login --tenant # Specific tenant, if your tenant is not the default az login --allow-no-subscriptions # Tenant without subscriptions az login --use-device-code # Remote/headless environments
To inspect the currently active Azure CLI account:
az account show
Dataverse environments
Environments are configured in environments.json in the project root and identified by friendly names such as dev, test, prod. Agents use these names when selecting which Dataverse environment to work with.
If an environment is in a different tenant than the default Azure CLI tenant, add a tenantId for that environment.
DATAVERSE_ENVIRONMENTS_PATH can be used to override the config file path at runtime.
Example:
{
"environments": { "dev": { "url": "https://YOUR-DEV.crm.dynamics.com/", "allowWrite": true }, "test": { "url": "https://YOUR-TEST.crm4.dynamics.com", "allowWrite": false }, "prod": { "url": "https://YOUR-PROD.crm4.dynamics.com", "allowWrite": false, "tenantId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" } } }
Add project-specific Copilot instructions
For better agent behavior, add Dataverse-specific instructions to the project where you use the MCP.
Copy docs/examples/copilot-instructions.md to .github/copilot-instructions.md in the workspace where you use the MCP server, then customize it for your project:
- replace
YOUR-dev,YOUR-test, andYOUR-prodwith the environment names fromenvironments.json - replace
YOUR_DEFAULT_SOLUTIONwith the unique name of the primary Dataverse solution - adjust the default environment and safety rules for the project
Commit the customized file to the project repository so all contributors use the same guidance.
Run directly with npx
The package can also be launched manually with stdio as default transport:
npx -y jaj-dataverse-dev-mcp
For development or clients that use Streamable HTTP add --http.
What can it be used for?
Although the MCP is intentionally a thin wrapper around the Dataverse Web API, it can support a broad range of development and troubleshooting tasks, for example:
- query and update Dataverse records
- inspect tables, columns, metadata, publishers, and solutions
- create and configure unmanaged solutions
- add existing components to solutions
- work with Dataverse actions and functions
- export solutions for deployment to other environments
This makes it useful for both direct development tasks and agent-driven workflows where the agent inspects Dataverse, decides on the next action, and performs it through the MCP.