Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Real-time Firestore schema context for AI coding agents. Stop hallucinating field names.
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。
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。
选择你使用的 AI 应用,然后复制设置。
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": {
"lintbase": {
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add lintbase -- npx -y lintbase-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"lintbase": {
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"lintbase": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}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。
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。
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。
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7日
发布者(Madia333)不是仓库所有者(lintbase);可能是其他人代为上架的
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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
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。
它在 1 项适用的基础检查中通过了 0 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。
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)。
GitHub 仓库最后更新于–,最新版本(0.1.3)于8个月前进入注册中心。
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。
同一分类中有:Spec Driven Development, Remote Desktop Commander, FactLot
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ 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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Cited CRE facts and deterministic calculators for agents: rent, CAM, debt sizing, IRR, parcel IDs.
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。
Give your AI coding agent real-time Firestore schema context — so it stops hallucinating field names.
When you ask Cursor, Claude, or Windsurf to write Firestore code, it guesses your schema.
// What your AI writes:
await db.collection('users').doc(id).update({ name: value });
// What's actually in your DB: { displayName, uid, email, createdAt, plan } ← no "name" field
The result: silent bugs, runtime crashes, and schema drift baked in by your AI assistant.
lintbase-mcp is a Model Context Protocol server that connects your AI agent directly to your Firestore database. Before writing any code, the agent checks what's actually there.
You: "Add a subscription status field to users"
Agent → lintbase_get_schema({ collection: "users" }) ← { uid, email, createdAt, plan, displayName }
Agent: "I can see users has uid, email, createdAt, plan, displayName. I'll add subscriptionStatus as a string field alongside plan..."
No hallucinations. No drift. Ground-truth schema, every time.
| Tool | What it does |
|---|---|
lintbase_scan | Full database audit — schema drift, security issues, performance problems, cost leaks. Returns a structured report with a 0–100 risk score. |
lintbase_get_schema | Returns real field names, types, and presence rates for one or all collections. Use before writing any DB code. |
lintbase_get_issues | Returns filtered issues. Ask targeted questions: *"any errors in users?"*, *"all security issues?"*, *"schema drift only?"* |
Install the LintBase Agent Skill into any project so your AI agent automatically uses LintBase before writing Firestore code — no prompting required:
npx -p lintbase-mcp lintbase-mcp-install-skill
This copies a SKILL.md file into .agent/skills/lintbase/ in your project. Cursor, Claude Code, Gemini CLI, and other MCP-compatible agents read this file on startup and follow its instructions automatically.
Same format as the Firebase official Agent Skills. ✅
You'll need a Firebase service account JSON key for your project.
Add to .cursor/mcp.json in your project root:
{
"mcpServers": { "lintbase": { "command": "npx", "args": ["-y", "lintbase-mcp"] } } }
Then in your conversation:
"Check my Firestore schema before writing this code. Key is at ./service-account.json"
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": { "lintbase": { "command": "npx", "args": ["-y", "lintbase-mcp"] } } }
Restart Claude Desktop and start a new conversation.
Add to your Windsurf MCP config:
{
"mcpServers": { "lintbase": { "command": "npx", "args": ["-y", "lintbase-mcp"] } } }
Once connected, your AI agent has three tools available. You can trigger them naturally in conversation or the agent will use them automatically when relevant.
"What fields are in the users collection?"
→ lintbase_get_schema({ keyPath: "./sa.json", collection: "users" })
Returns:
## users (50 docs sampled)
| Field | Type | Presence | Stable |
|---|---|---|---|
| uid | string | 100% | ✅ |
| string | 100% | ✅ | |
| createdAt | timestamp | 100% | ✅ |
| plan | string | 95% | ✅ |
| displayName | string | 62% | ⚠️ ← mark as optional |
"Run a full LintBase scan before I start this refactor"
→ lintbase_scan({ keyPath: "./sa.json", sampleSize: 100 })
Returns: Complete report with risk score, all issues across 4 analyzers (schema drift, security, performance, cost).
"Any blocking errors I should know about?"
→ lintbase_get_issues({ keyPath: "./sa.json", severity: "error" })
"Are there any security problems with my database?"
→ lintbase_get_issues({ keyPath: "./sa.json", rule: "security/" })
stdio process spawned by your IDElintbase_get_issues| Filter | Values | Example |
|---|---|---|
severity | error, warning, info | Only blocking errors |
collection | any collection name | Only issues in users |
rule | rule prefix | schema/, security/, perf/, cost/ |
| Rule | Analyzer | What it catches |
|---|---|---|
schema/field-type-mismatch | Schema | Same field holds multiple types across documents |
schema/sparse-field | Schema | Field missing from >40% of documents |
schema/high-field-variance | Schema | Wildly different field counts between documents |
security/sensitive-collection | Security | Collection name suggests unprotected PII |
security/field-contains-secret | Security | Field name suggests stored secrets/tokens |
security/debug-data-in-production | Security | Debug/test collections left in production |
perf/excessive-nesting | Performance | Documents nested >5 levels deep |
perf/document-too-large | Performance | Documents approaching Firestore's 1MB limit |
cost/large-avg-document | Cost | Average document size driving up bandwidth costs |
cost/logging-sink | Cost | Collection used as an unbounded write sink |
npx lintbase scan firestore --key ./sa.json for a full terminal report.MIT © Mamadou Dia