Supabase
MCP server for interacting with the Supabase platform
作者提供的说明(英文原文)
Read-only Kafka access for AI assistants: topics, consumer groups, schemas, cluster health.
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。
CONDUKTOR_CONSOLE_URL, CONDUKTOR_API_TOKEN, Authorization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 …”。
这个地址需要在请求的 header 中放入密钥,而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"kafka": {
"command": "npx",
"args": [
"-y",
"@conduktor/mcp"
],
"env": {
"CONDUKTOR_CONSOLE_URL": "your-value",
"CONDUKTOR_API_TOKEN": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add --transport http kafka 'https://{console_host}/api/mcp' --header 'Authorization: Bearer {token}'claude mcp add kafka --env CONDUKTOR_CONSOLE_URL=your-value --env CONDUKTOR_API_TOKEN=your-value -- npx -y @conduktor/mcp运行前请先填写空着的值。
地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"kafka": {
"url": "https://{console_host}/api/mcp",
"headers": {
"Authorization": "Bearer {token}"
}
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"kafka": {
"command": "npx",
"args": [
"-y",
"@conduktor/mcp"
],
"env": {
"CONDUKTOR_CONSOLE_URL": "your-value",
"CONDUKTOR_API_TOKEN": "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。
地址中有只有你知道的 {…} 部分(例如你的账户),请复制后自行填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"kafka": {
"type": "http",
"url": "https://{console_host}/api/mcp",
"headers": {
"Authorization": "Bearer {token}"
}
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"kafka": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@conduktor/mcp"
],
"env": {
"CONDUKTOR_CONSOLE_URL": "your-value",
"CONDUKTOR_API_TOKEN": "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。
这个地址需要在请求的 header 中放入密钥,而添加连接器的菜单无法设置。请改用 Claude Code、Cursor 或 VS Code。
你的选择通过 Cookie 保存在这台设备上,无需账户。
Authorization 必填 HTTP header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。
我们还没有读取这个服务器的工具列表;我们会轮流检查各个端点。
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8日
注册名称已通过 GitHub 账户 conduktor 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
这个地址需要填入你自己的账户信息,所以我们无法替你检查
这个地址需要填入你自己的账户信息,所以我们无法替你检查
尚未检查;我们会在检查端点时读取工具列表
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:CONDUKTOR_CONSOLE_URL, CONDUKTOR_API_TOKEN, Authorization
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(1.0.1)于32天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
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
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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。
Put an AI assistant in charge of your Kafka estate.
Not a chat window onto a cluster. A way for agents — yours, or ours — to see what is actually happening across every cluster you run, and to act on it: reclaim the topics nobody consumes, attribute cost back to the teams spending it, catch the certificate expiring next month, restart the connector that failed at 3am, label the topics whose owner left the company.
The MCP endpoint ships inside Conduktor Console. There is nothing to deploy, and nothing new to secure: the assistant inherits the RBAC, audit trail and ownership model you already run.
No Kafka to hand? demo/ brings up a cluster, Console and the MCP endpoint with docker compose up -d, seeded with topics, traffic and a consumer group that is deliberately behind.
Create a Personal Access Token in Console, then point your MCP client at your own Console:
{
"mcpServers": { "conduktor": { "command": "npx", "args": ["-y", "@conduktor/mcp"], "env": { "CONDUKTOR_CONSOLE_URL": "https://console.acme-corp.com", "CONDUKTOR_API_TOKEN": "your-personal-access-token" } } } }
/api/mcp is appended for you. Pasting a URL that already ends in it works too.
Connecting without the package
@conduktor/mcp wraps mcp-remote. Call it yourself if you prefer:
{
"mcpServers": { "conduktor": { "command": "npx", "args": [ "mcp-remote", "https://console.acme-corp.com/api/mcp", "--header", "Authorization: Bearer ${CONDUKTOR_API_TOKEN}" ], "env": { "CONDUKTOR_API_TOKEN": "your-personal-access-token" } } } }
Ask in English, get answers grounded in your actual estate rather than in documentation:
*Which topics haven't been consumed in a month, and who created them?*
*What did the payments team cost us last quarter, and what drove it?*
*This consumer group is stuck — what's the blocking message?*
*Which certificates expire before March, and whose are they?*
*Nobody owns these topics. Can you work out who should, from the lineage?*
Those are not demos. Each maps onto tools below, and onto tasks an agent can run unattended.
The same catalogue serves two kinds of caller. Your tools — Claude Code, Cursor, scripts, your internal developer platform — and agents running inside Console, on a schedule or on an audit-log event, under their own machine identity.
That second one is where this stops being a chatbot. An agent is a task plus an identity plus a bounded set of tools. It wakes up on Monday at 9am, or the moment a connector fails, works the problem, and comes back with something you can act on — a report, a recommendation, or a mutation it proposes and you approve.
Every run is traced end to end: the prompt, each tool call, the tokens, the result. That is what you attach to a ticket, or hand to an auditor.
Thirty-one, across the whole control plane. Most read; some write, and that set is growing.
| Tool | What it does |
|---|---|
list-clusters | Every Kafka cluster this Console manages, with its Schema Registry, Connect clusters and flavor |
get-cluster | One cluster's registration by slug |
insights-cluster | Health score, topic and partition counts, serialization breakdown — the shape of a cluster in one call |
| Tool | What it does |
|---|---|
list-topics | Topic catalogue: names, labels, descriptions, partitions, replication, configs |
get-topic | One topic by exact name |
list-topics-with-usage | Topics with message count, size and throughput |
query-topics | Filter topics across a cluster |
aggregate-topics | Group topics and compute a statistic per group — count, sum, average, min, max |
insights-topics | Partition skew, replication problems, and what is quietly wrong |
set-topic-labels (writes) | Label topics — ownership, environment, whatever your taxonomy is |
| Tool | What it does |
|---|---|
get-last-messages | The last N messages from a topic |
get-record-at | A specific record, by partition and offset — the one blocking a consumer |
| Tool | What it does |
|---|---|
list-consumer-groups | Groups with state, lag and member count |
get-consumer-group | One group in detail |
list-consumer-groups-by-topic | Who actually reads this topic |
| Tool | What it does |
|---|---|
list-subjects · get-subject · list-subject-names | The subject catalogue |
get-schema-version | A specific version |
check-schema-compatibility | Whether a change breaks consumers, before it ships |
| Tool | What it does |
|---|---|
list-connectors-detailed · get-connector | Connectors and their state |
list-interceptors | Gateway interceptors configured on a cluster |
| Tool | What it does |
|---|---|
list-acl-bindings | Who is allowed to do what |
list-service-accounts · get-service-account | Non-human identities |
list-certificates | Certificates and their expiry |
list-applications | Applications registered in the catalogue |
get-stream-lineage | What flows into what |
query-audit-log | Who did what, when |
get-chargeback-report | Cost attributed per team |
Giving an agent real access to production is only reasonable because the boundaries already exist and are enforced per call.
It is you. Every call carries a Console token and runs under that user's RBAC. An agent cannot see a cluster its owner cannot see. Revoke the owner's access and the agent loses it at the same instant.
Control plane, not data plane. What reaches the model is metadata — topics, configs, offsets, groups, schemas, lineage, cost. Your records stay in Kafka unless a task explicitly needs a tool that reads them.
Everything is audited. Every call lands in the Console audit log, attributable to the identity that made it.
The launcher published as @conduktor/mcp, this documentation, and the registry metadata. The server itself runs inside Console. For bugs in the server use Conduktor support; for the launcher or these docs, open an issue here.