Supabase
MCP server for interacting with the Supabase platform
作者提供的说明(英文原文)
The trust and intelligence layer between AI agents and your database.
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。
DATABASE_URL标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。
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": {
"schemabrain": {
"command": "uvx",
"args": [
"schemabrain",
"serve",
"--url-env",
"DATABASE_URL",
"--store-path",
"{store_path}"
],
"env": {
"DATABASE_URL": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add schemabrain --env DATABASE_URL=your-value -- uvx schemabrain serve --url-env DATABASE_URL --store-path '{store_path}'运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"schemabrain": {
"command": "uvx",
"args": [
"schemabrain",
"serve",
"--url-env",
"DATABASE_URL",
"--store-path",
"{store_path}"
],
"env": {
"DATABASE_URL": "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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"schemabrain": {
"type": "stdio",
"command": "uvx",
"args": [
"schemabrain",
"serve",
"--url-env",
"DATABASE_URL",
"--store-path",
"{store_path}"
],
"env": {
"DATABASE_URL": "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。
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
注册名称已通过 GitHub 账户 Arun-kc 验证,该账户拥有此仓库
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 1 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。
你需要填写:DATABASE_URL
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(0.6.0)于5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 Python (uv)。
同一分类中有:Supabase, MongoDB MCP Server, Elasticsearch MCP Server
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 for interacting with the Supabase platform
MongoDB Model Context Protocol Server
MCP server for interacting with Elasticsearch
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。
Stop giving AI agents raw database connection strings.
Give them SchemaBrain instead — a read-only trust and intelligence layer where the agent never writes SQL, PII is refused before the query runs, and every call lands in a tamper-evident audit log.
Works with Claude Desktop · Claude Code · Cursor · Windsurf · any MCP host
SchemaBrain compiles every query from definitions you control — no path from a prompt to raw SQL at your database.
Three guarantees that close the trust gap between AI agents and your database:
execute() tool, no query() tool, no path from agent prompt to a write at your database.audit verify exits non-zero if any past row was rewritten.See it in action — ask for something the schema can't answer, and it refuses instead of fabricating a join:
You: *compute usage volume by plan tier*
SchemaBrain → agent:
{ "kind": "unreachable_entity", "recovery": { "suggested_tool": "resolve_join" } }— there's noplan_idon usage events, so it won't invent one.
Claude: I can't fake that join — here's contracted revenue by plan tier instead, which actually resolves. ✓
→ Full session, with the SQL and results
Watch it run — a live Postgres schema becomes a governed knowledge graph, the firewall computes the safe metric and refuses the leaks, and every call lands in a tamper-evident audit log. No agent, no API key:
uvx schemabrain init
Cost: $0 to run the bundled demo (pre-curated pack, no API key) · ~$0.03 to LLM-index a fresh 84-column schema · $0 to re-index unchanged schemas. Detail in Sample session.
Status: 0.6.0 (beta). Postgres supported today (the local store itself is SQLite). SQLite / Snowflake / BigQuery / MySQL source connectors on the roadmap.
Read next based on what you need:
| Goal | Where to go |
|---|---|
| Try it on the bundled fixture | Quickstart |
| Understand the safety guarantees | Safety guarantees |
| Wire up your MCP client | Claude Desktop · Claude Code · Cursor · Windsurf · Cline · ChatGPT (roadmap) |
| Plug into your own agent loop | docs/setup/manual.md |
| Build a semantic layer | docs/semantic-layer.md |
| Run in production (audit, drift, Docker) | docs/operations.md |
| Observe the agent (tail, audit log, OTel) | docs/observability.md |
| Compare with Querybear / Anthropic reference Postgres MCP | vs Querybear · vs Anthropic reference |
| Compare with Vanna / Atlan / dbt-mcp / WrenAI | docs/landscape.md |
Just want to see what it does?
uvx schemabrain demo— one command, zero prompts. Builds the sample SaaS layer, then lets you open the dashboard or run a terminal firewall showcase. No API key, and no Docker for the dashboard / showcase paths. The steps below are for wiring SchemaBrain into your own agent against your own database.
Three steps from uvx schemabrain init to a working Claude Desktop integration. If you paste your own Postgres URL — no Docker needed, ~30s. Press Enter for the bundled demo and init invokes Docker + downloads a ~67 MB embedding model first time; ~45s once cached.
uvx schemabrain init # zero-install: runs the wizard in one shot
pipx install schemabrain # (or) pip install schemabrain schemabrain --version
Source install (git clone + uv sync --extra dev) is documented in docs/setup.md.
schemabrain init
init is a seven-stage wizard that takes you from "I have a Postgres database" to "Claude Desktop can answer questions about it" in one command. On first run it prompts for what it needs:
schemabrain entities suggest --apply once you have a key.SchemaBrain init — activation wizard
[1/7] Source check ✓ source reachable + read-only [2/7] Index schema ✓ 12 tables, 84 columns indexed [3/7] Curate entities ✓ 12 entities applied (bundled demo pack) [4/7] Curate metrics ✓ 5 metrics applied (bundled demo pack) [5/7] Curate joins ✓ 11 canonical joins applied (bundled demo pack) [6/7] Wire host ✓ wrote schemabrain entry to claude_desktop_config.json (default; switch with --host claude-code|cursor|windsurf|manual) [7/7] Next ✓ restart your MCP host, then ask: "list the entities SchemaBrain knows about"
Full wizard reference (stages explained, flags, dbt auto-detection, --print-only for non-Claude-Desktop hosts, --no-entities / --no-metrics / --no-joins opt-outs, cost-cap pauses): docs/setup.md.
list the entities SchemaBrain knows about
If Claude calls list_entities and reports user, order, etc., you're done. If not, see Troubleshooting.
After the wizard, schemabrain inspect shows what the agent has and schemabrain tail streams every tool call live — see docs/operations.md.
init writes just ./schemabrain.db (the local store — gitignore it) plus your host config. To tune the PII policy and semantic layer as editable YAML, re-run with --emit-yaml-dir:
schemabrain init --url-env DATABASE_URL --emit-yaml-dir ./schemabrain
Edit a file, schemabrain apply ./schemabrain, schemabrain check to validate, restart serve. There is no schemabrain.yaml — config is CLI flags + SCHEMABRAIN_* env vars (auto-loaded from .env) + that YAML tree. Full map: Your project.
Six properties SchemaBrain enforces at the SQL boundary today:
The MCP surface exposes twelve tools — none of which can write. No execute(), no query(), no path from agent prompt to a write at your database, regardless of session state — the guarantee is structural, not a flag the agent can flip. schemabrain serve also pins default_transaction_read_only=on as belt-and-suspenders. Read-only by architecture →
get_metric tool boundaryAny get_metric touching a blocked PII category returns a refused envelope — the compiled SQL never runs and the refusal lands in mcp_audit. describe_entity enforces the same at the column level (blocked columns ship redacted=True). init blocks the catastrophic-leak set by default (credential,payment_card,government_id); --pii-block replaces the set, so widen by listing the full target. Detection is column-name pattern matching across twelve GDPR / CCPA / HIPAA / PCI categories; content-aware classification is on the roadmap. PII taxonomy & propagation →
Every tool call writes one row to an append-only mcp_audit table — PII categories, content-addressable fingerprints, sha256 hash chain. audit verify re-walks the chain and exits non-zero if any past row was rewritten.
schemabrain audit verify # exit 0 = chain clean
Tamper-evident audit chain →
Every non-success call — refused, error, or degraded — returns a structured recovery.suggested_args block, not a message to parse. PII blocks (status: "refused") ship the entity to ret
只显示了开头部分;其余内容请在仓库中阅读