MCP server that gives AI coding agents live Firestore schema context, including field names, types, and presence rates.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent โ or use 1-click editor setup below.
The install command below started, but didn't respond the way we expected when we tried to talk to it.
npx -y lintbaseinitialize succeeded but no response to tools/list.
This is an experimental automated check and can have false negatives โ missing environment variables, a slow cold install, etc. It doesnโt necessarily mean somethingโs wrong. Last checked 1mo ago.
๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Lintbase MCP.
Ground Truth for AI Coding Agents. LintBase gives AI agents real-time knowledge of your database schema, security rules, and architecture so they stop hallucinating your codebase.
Developers are constantly feeding context files to AI tools like Cursor, Windsurf, Copilot Workspace, and Claude Code. If your agent doesn't understand your real database schema, it writes code that fails in production.
LintBase acts as the bridge. It connects directly to your database, reads the ground truth of your live documents, and generates structured context optimized for AI.
lintbase check against schema snapshots.Because in document databases, the code lies. The real schema is whatever your live documents actually contain, and that drifts away from the code with every half-finished migration, every renamed field, and every previous AI session that wrote in a hurry. An agent inferring the schema from TypeScript interfaces writes plausible queries against a database that no longer exists. The failure is silent: empty results, undefined values, a new field variant living alongside the old one. LintBase reads the documents, not the code.
The fastest way to give your AI agent perfect database knowledge.
Output:
Drop the lintbase-context folder into your AI's context window, or mention it in .cursorrules. Your agent will now write perfect, drift-free database queries.
Firebase Console โ Project Settings โ Service Accounts โ Generate new private key
Save the JSON file. Never commit it to git.
LintBase acts as "Version Control for your Schema". Run the snapshot command to create a baseline:
Commit .lintbase/schema.json to your repository. Then, add the check command to your CI/CD pipeline (GitHub Actions, GitLab CI):
If a query or deployment accidentally deletes a critical field or changes a type (e.g., string to number), your CI build will fail instantly.
You'll see a full report in your terminal:
Track your database health over time at lintbase.com:
Get your token at lintbase.com/dashboard/settings.
npx lintbase scan firestore --key ./sa.jsonnpx lintbase scan mongodb --uri mongodb+srv://user:pass@cluster.mongodb.net/testUsing Cursor, Claude Desktop, or Windsurf? Install lintbase-mcp to give your AI agent real-time Firestore schema context โ so it stops hallucinating field names.
Add to .cursor/mcp.json:
Now when you ask your AI "add a field to users", it will check your real schema first before writing a line of code.
โ Full setup guide & tools reference
| Rule | What it detects |
|---|---|
no-auth-check | Collections readable/writable without auth |
exposed-pii | Email, phone, SSN fields without encryption markers |
world-readable | Documents with overly permissive security rules |
| Rule | What it detects |
|---|---|
large-collection | Collections with 100k+ docs and high read cost |
unbounded-query | Queries without limit() that scan entire collections |
missing-index | Filter combinations that fall back to full collection scans |
debug-collection | Collections that look like temporary data that was never cleaned up |
| Rule | What it detects |
|---|---|
type-inconsistency | Field stored as different types across documents |
missing-required-field | Field present in 90%+ of docs but absent in some |
nullable-id | Reference fields that are sometimes null |
| Rule | What it detects |
|---|---|
deep-nesting | Document fields nested > 3 levels deep |
large-document | Documents approaching the 1MB Firestore limit |
hot-document | Single document updated by many users simultaneously |
no-pagination | Collections without a standard pagination field |
The CLI is free forever. The dashboard visualizes your scan results as an interactive schema map โ your credentials never leave your machine.
What Pro gets you via --save:
CLI Local Tooling: 100% Free ยท Pro: $39/month โ unlimited history, dashboards, and shared team workflow.
--limit (default 100) to prevent accidental read costs--save flag only sends the scan summary and issue list โ never raw document dataMIT ยฉ Mamadou Dia
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