Supabase 데이터베이스와 상호 작용하기 위한 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 MCP 인터페이스를 통해 테이블 쿼리 및 TypeScript 유형 생성 도구를 제공합니다.
쿼리 테이블 : 다음을 지원하여 모든 테이블에 대한 쿼리를 실행합니다.
스키마 선택
열 필터링
여러 연산자가 있는 Where 절
쪽수 매기기
오류 처리
유형 생성 : 데이터베이스에 대한 TypeScript 유형을 생성합니다.
모든 스키마(public, auth, api 등) 지원
로컬 및 원격 Supabase 프로젝트 모두에서 작동합니다.
콘솔에 직접 출력
자동 프로젝트 참조 감지
Related MCP server: SchemaFlow MCP Server
Node.js(v16 이상)
Supabase 프로젝트(로컬 또는 호스팅)
Supabase CLI(유형 생성용)
저장소를 복제합니다.
지엑스피1
종속성 설치:
npm installSupabase CLI를 설치합니다(유형 생성에 필요):
# Using npm
npm install -g supabase
# Or using Homebrew on macOS
brew install supabase/tap/supabaseSupabase 자격증을 받으세요:
호스팅 프로젝트의 경우:
Supabase 프로젝트 대시보드로 이동하세요
프로젝트 설정 > API로 이동하세요.
프로젝트 URL과 service_role 키를 복사하세요(anon 키가 아님)
로컬 프로젝트의 경우:
로컬 Supabase 인스턴스를 시작하세요
로컬 URL을 사용하세요(일반적으로 http://localhost:54321 )
로컬 service_role 키를 사용하세요
환경 변수 구성:
# Create a .env file (this will be ignored by git)
echo "SUPABASE_URL=your_project_url
SUPABASE_KEY=your_service_role_key" > .env서버를 빌드하세요:
npm run buildClaude Desktop 설정 열기:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
리눅스: ~/.config/Claude/claude_desktop_config.json
서버 구성을 추가합니다.
{
"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"
}
}
}
}VSCode 설정 열기:
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
리눅스: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
서버 구성을 추가합니다(Claude Desktop과 동일한 형식).
// 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>스키마 선택과 WHERE 절 지원을 사용하여 특정 테이블을 쿼리합니다.
매개변수:
schema (선택 사항): 데이터베이스 스키마(기본값은 public)
table (필수): 쿼리할 테이블의 이름
select (선택 사항): 쉼표로 구분된 열 목록
where (선택 사항): 다음 조건을 포함하는 조건 배열:
column : 열 이름
operator : eq, neq, gt, gte, lt, lte, like, ilike, is 중 하나
value : 비교할 값
Supabase 데이터베이스 스키마에 대한 TypeScript 유형을 생성합니다.
매개변수:
schema (선택 사항): 데이터베이스 스키마(기본값은 public)
Supabase CLI가 설치되어 있는지 확인하세요.
supabase --version로컬 프로젝트의 경우:
로컬 Supabase 인스턴스가 실행 중인지 확인하세요.
service_role 키가 올바른지 확인하세요
호스팅 프로젝트의 경우:
프로젝트 참조가 올바른지 확인하세요(URL에서 추출)
anon 키가 아닌 service_role 키를 사용하고 있는지 확인하세요.
스키마와 테이블 이름을 확인하세요
select 및 where 절에서 열 이름 확인
service_role 키에 필요한 권한이 있는지 확인하세요.
저장소를 포크하세요
기능 브랜치를 만듭니다: git checkout -b feature/my-feature
변경 사항을 커밋하세요: git commit -am 'Add my feature'
브랜치에 푸시: git push origin feature/my-feature
풀 리퀘스트 제출
MIT 라이센스 - 자세한 내용은 라이센스 파일을 참조하세요.
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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