Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Real-time Firestore schema context for AI coding agents. Stop hallucinating field names.
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We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"lintbase": {
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}Run this in your terminal
claude mcp add lintbase -- npx -y lintbase-mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"lintbase": {
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"lintbase": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"lintbase-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The publisher (Madia333) is not the repo owner (lintbase); someone else may have listed it
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 0 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.3) reached the registry 8 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, TF Rooms
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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.
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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