用于与 Supabase 数据库交互的模型上下文协议 (MCP) 服务器。该服务器提供通过 MCP 接口查询表和生成 TypeScript 类型的工具。
查询表：对任何表执行查询，支持以下内容：
模式选择
列过滤
具有多个运算符的 Where 子句
分页
错误处理
类型生成：为您的数据库生成 TypeScript 类型：
支持任何模式（公共、授权、API 等）
适用于本地和远程 Supabase 项目
直接输出到控制台
自动项目参考检测
Related MCP server: SchemaFlow MCP Server
Node.js（v16 或更高版本）
Supabase 项目（本地或托管）
Supabase CLI（用于类型生成）
克隆存储库：
git clone https://github.com/yourusername/supabase-mcp-server.git
cd supabase-mcp-server安装依赖项：
npm install安装 Supabase CLI（类型生成所需）：
# Using npm
npm install -g supabase
# Or using Homebrew on macOS
brew install supabase/tap/supabase获取您的 Supabase 凭证：
对于托管项目：
转到您的 Supabase 项目仪表板
导航至项目设置 > API
复制项目 URL 和 service_role 密钥（不是 anon 密钥）
对于本地项目：
启动本地 Supabase 实例
使用本地 URL（通常为http://localhost:54321 ）
使用本地 service_role 密钥
配置环境变量：
# Create a .env file (this will be ignored by git)
echo "SUPABASE_URL=your_project_url
SUPABASE_KEY=your_service_role_key" > .env构建服务器：
npm run build打开Claude桌面设置：
macOS： ~/Library/Application Support/Claude/claude_desktop_config.json
Windows： %APPDATA%\Claude\claude_desktop_config.json
Linux： ~/.config/Claude/claude_desktop_config.json
添加服务器配置：
{
"mcpServers": {
"supabase": {
"command": "node",
"args": ["/absolute/path/to/supabase-mcp-server/build/index.js"],
"env": {
"SUPABASE_URL": "your_project_url",
"SUPABASE_KEY": "your_service_role_key"
}
}
}
}打开 VSCode 设置：
macOS： ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
Windows： %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
Linux： ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
添加服务器配置（与 Claude Desktop 相同的格式）。
// Query with schema selection and where clause
<use_mcp_tool>
<server_name>supabase</server_name>
<tool_name>query_table</tool_name>
<arguments>
{
"schema": "public",
"table": "users",
"select": "id,name,email",
"where": [
{
"column": "is_active",
"operator": "eq",
"value": true
}
]
}
</arguments>
</use_mcp_tool>// Generate types for public schema
<use_mcp_tool>
<server_name>supabase</server_name>
<tool_name>generate_types</tool_name>
<arguments>
{
"schema": "public"
}
</arguments>
</use_mcp_tool>使用模式选择和 where 子句支持查询特定表。
参数：
schema （可选）：数据库模式（默认为公共）
table （必填）：要查询的表的名称
select （可选）：以逗号分隔的列列表
where （可选）：条件数组，包含：
column ：列名
operator ：eq、neq、gt、gte、lt、lte、like、ilike、is 之一
value ：要比较的值
为您的 Supabase 数据库模式生成 TypeScript 类型。
参数：
schema （可选）：数据库模式（默认为公共）
确保已安装 Supabase CLI：
supabase --version对于本地项目：
确保您的本地 Supabase 实例正在运行
验证您的 service_role 键是否正确
对于托管项目：
确认您的项目参考正确（从 URL 中提取）
验证您使用的是 service_role 密钥，而不是 anon 密钥
检查您的架构和表名称
验证 select 和 where 子句中的列名
确保您的 service_role 密钥具有必要的权限
分叉存储库
创建你的功能分支： git checkout -b feature/my-feature
提交您的更改： git commit -am 'Add my feature'
推送到分支： git push origin feature/my-feature
提交拉取请求
MIT 许可证 - 详情请参阅许可证文件
Generate TypeScript types for your Supabase database schema
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | Database schema (optional, defaults to public) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it works - whether it connects to a live database, reads from configuration files, requires authentication, has rate limits, or what format the output takes. The description is functional but lacks operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately sized for a tool with one simple parameter and gets straight to the point.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that presumably generates code/types from a database schema, the description is minimal. With no annotations and no output schema, it doesn't explain what the output looks like (TypeScript files? Inline code?), how errors are handled, or any dependencies or requirements. The description is functional but lacks important context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the input schema already documents the single optional 'schema' parameter with its default value. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Generate TypeScript types') and target resource ('your Supabase database schema'), providing a complete purpose statement. It distinguishes from the sibling tool 'query_table' which appears to be for data querying rather than schema type generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor any context about prerequisites or constraints. While it's clear what the tool does, there's no information about appropriate use cases or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query a specific table with schema selection and where clause support
| Name | Required | Description | Default |
|---|---|---|---|
| schema | No | Database schema (optional, defaults to public) | |
| table | Yes | Name of the table to query | |
| select | No | Comma-separated list of columns to select (optional, defaults to *) | |
| where | No | Array of where conditions (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool queries a table but doesn't mention whether this is read-only (likely but not confirmed), what permissions are required, whether it supports pagination/limits, error handling, or what the output format looks like. The description adds minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for the tool's complexity and front-loads the main purpose immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query tool with 4 parameters and no annotations or output schema, the description is insufficient. It doesn't address key contextual elements like: whether this is a safe read operation, what authentication/permissions are needed, how results are returned (format, size limits), error conditions, or relationship to the sibling tool. The 100% schema coverage helps with parameters but doesn't compensate for missing behavioral context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description mentions 'schema selection and where clause support' which aligns with parameters in the schema but doesn't add meaningful semantic context beyond what's already in the parameter descriptions. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Query') and resource ('a specific table'), with additional functionality mentioned ('schema selection and where clause support'). It distinguishes from the sibling 'generate_types' by focusing on data retrieval rather than type generation. However, it doesn't specify if this is a read-only query or might modify data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description mentions functionality but doesn't indicate scenarios where this tool is appropriate, prerequisites for use, or limitations compared to other query methods. The sibling tool 'generate_types' serves a completely different purpose, so no comparative guidance is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_typesquery_tableScored across 2 tools
The two tools have clearly distinct purposes: generate_types focuses on TypeScript type generation from the database schema, while query_table handles data retrieval from specific tables with filtering. There is no overlap or ambiguity between these functions.
Both tools follow a consistent verb_noun naming pattern (generate_types and query_table), using snake_case throughout. The naming is predictable and readable without any deviations.
With only 2 tools, the server feels under-scoped for a Supabase integration, which typically involves operations like create, update, delete, and schema management beyond just querying and type generation. This limited set may hinder agent workflows.
The server lacks essential CRUD operations (e.g., insert, update, delete) and other Supabase features like authentication or real-time subscriptions, making it severely incomplete for typical database interactions. Agents will face significant gaps in functionality.
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