이 프로젝트는 모델 컨텍스트 프로토콜 표준을 따르므로 AI 어시스턴트가 Square의 Connect API와 상호 작용할 수 있습니다.
npx를 사용하여 Square MCP 서버를 시작하고 실행하세요.
지엑스피1
YOUR_SQUARE_ACCESS_TOKEN 실제 Square 액세스 토큰으로 바꾸세요. Square 액세스 토큰 가이드에 따라 액세스 토큰을 얻을 수 있습니다. 명령을 실행하기 전에 환경 변수를 설정할 수도 있습니다.
Related MCP server: AI-Assisted CRM MCP Server
Square는 이제 다음 위치에서 호스팅되는 원격 MCP 서버를 제공합니다.
https://mcp.squareup.com/sse원격 MCP는 OAuth 인증을 사용하므로 액세스 토큰을 수동으로 만들거나 관리하지 않고도 Square 계정으로 직접 로그인할 수 있으므로 권장됩니다.
환경 변수 | 목적 | 예 |
| Square API 액세스 토큰 |
|
| Square 샌드박스 환경 사용 |
|
| Square 프로덕션 환경 사용 |
|
| 읽기 전용 작업으로 제한 |
|
| Square API 버전 지정 |
|
Goose 로 Square MCP 서버를 구성하려면:
Goose에 Square 원격 MCP를 설치하려면 Goose가 설치된 컴퓨터에서 이 URL을 클릭하세요.
goose://extension?cmd=npx&arg=mcp-remote&arg=https%3A%2F%2Fmcp.squareup.com%2Fsse&id=square_mcp_production_remote&name=Square%20MCP%20Remote&description=Square%20Production%20MCP%20Remote
또는 URL을 복사하여 브라우저의 주소창에 붙여넣으세요.
# Automatic installation
npx square-mcp-server install
# Get URL for manual installation
npx square-mcp-server get-goose-urlinstall 명령은 자동으로 Goose 구성을 업데이트합니다.
Claude Desktop 통합에 대한 자세한 내용은 모델 컨텍스트 프로토콜 빠른 시작 가이드를 참조하세요. claude_desktop_config.json 파일에 다음 구성을 추가하세요.
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["mcp-remote", "https://mcp.squareup.com/sse"]
}
}
}이 접근 방식을 사용하면 액세스 토큰을 관리할 필요 없이 Square 계정 자격 증명으로 직접 인증할 수 있습니다.
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["square-mcp-server", "start"],
"env": {
"ACCESS_TOKEN": "YOUR_SQUARE_ACCESS_TOKEN",
"SANDBOX": "true"
}
}
}
}Square MCP 서버는 Square API와 상호 작용하기 위한 간소화된 도구 세트를 제공합니다.
도구 | 설명 | 주요 용도 |
| 서비스에 사용 가능한 방법을 알아보세요 | 탐험과 발견 |
| 자세한 매개변수 요구 사항을 확인하세요 | 요청 준비 |
| Square에 API 호출을 실행합니다. | 작업 수행 |
Square MCP 서버는 Square의 전체 API 생태계 에 대한 액세스를 제공합니다. 각 서비스에 대한 자세한 내용은 Square API 문서를 참조하세요.
서비스 | 설명 |
| Apple Pay 통합 |
| 은행 계좌 관리 |
| 예약을 위한 사용자 정의 속성 |
| 약속 예약 관리 |
| 결제 카드 관리 |
| 금전함 관리 |
| 카탈로그 관리(품목, 카테고리 등) |
| 체크아웃 및 결제 처리 |
| 고객을 위한 사용자 정의 속성 |
| 고객 그룹화 |
| 고객 세분화 |
| 고객 관리 |
| 스퀘어 기기 관리 |
| 결제 분쟁 처리 |
| 이벤트 추적 |
| 기프트 카드 활동 추적 |
| 기프트 카드 관리 |
| 재고 추적 |
| 송장 관리 |
| 인력 관리 |
| 위치에 대한 사용자 정의 속성 |
| 위치 관리 |
| 로열티 프로그램 관리 |
| 판매자를 위한 사용자 정의 속성 |
| 가맹점 계정 관리 |
| 입증 |
| 주문에 대한 사용자 정의 속성 |
| 주문 관리 |
| 결제 처리 |
| 지급 관리 |
| 환불 관리 |
| 웹사이트 통합 |
| Square Online 코드 통합 |
| 구독 관리 |
| 직원 관리 |
| 스퀘어 터미널 관리 |
| 공급업체 관리 |
| 이벤트 알림 |
MCP를 통해 Square API와 최적의 상호작용을 위해:
발견 : get_service_info 사용하여 사용 가능한 방법을 탐색합니다.
get_service_info(service: "catalog")이해 : get_type_info 사용하여 매개변수 요구 사항을 알아보세요
get_type_info(service: "catalog", method: "list")실행 : make_api_request 사용하여 작업을 수행합니다.
make_api_request(service: "catalog", method: "list", request: {})MCP Inspector는 테스트를 위한 시각적 인터페이스를 제공합니다.
# Build the project
npm run build
# Start the inspector with the Square MCP Server
npx @modelcontextprotocol/inspector node dist/index.js start저장소를 복제합니다
종속성 설치: npm install
개발 모드 시작: npm run watch
서버를 실행합니다: node dist/index.js start
MCP 검사기를 사용하여 변경 사항을 테스트하세요
이 저장소는 Square의 OpenAPI 사양을 기반으로 자동 생성됩니다. 기여는 환영하지만, 변경 사항은 이 코드를 생성하는 생성기에 반영되어야 합니다. 풀 리퀘스트를 제출하기 전에 이슈를 열어 제안된 변경 사항에 대해 논의해 주세요.
Get information about a Square API service. Call me before trying to get type info
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes | The Square API service category (e.g., 'catalog', 'payments') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavior. It only says 'get information', without mentioning whether it's read-only, idempotent, or any side effects. Minimal transparency.
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?
Two efficient sentences: first states purpose, second provides usage guidance. No wasted words.
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?
Adequate for a simple info tool with one parameter, but lacks description of the output format or any additional 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 has 100% description coverage for the only parameter, so baseline is 3. Tool description does not add extra meaning beyond the schema parameter description.
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?
Describes a specific action: getting info about a Square API service. Explicitly differentiates from sibling tool get_type_info by telling the agent to call this before that.
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?
Gives clear directive to call this before get_type_info, indicating proper ordering. However, no guidance on when not to use or alternatives like make_api_request.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get type information for a Square API method. You must call this before calling the make_api_request tool.
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes | The Square API service category (e.g., 'catalog', 'payments') | |
| method | Yes | The API method to call (e.g., 'list', 'create') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavior. It states it 'gets type information' but does not disclose whether it is read-only, any side effects, or what the response structure looks like, leaving significant gaps.
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?
Two sentences, front-loaded with purpose and a clear usage instruction. Every sentence adds value with no wasted words.
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 simple prerequisite tool, the description is acceptable but lacks detail on return values and behavioral context. Given no output schema, the agent might need more info to effectively use the result.
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 coverage is 100%, with both parameters described. The description adds no additional information beyond the schema, so baseline 3 is appropriate.
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 it gets type information for a Square API method, and the prerequisite relationship with make_api_request distinguishes it from sibling tools, though it does not specify what 'type information' entails.
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 explicitly instructs the agent to call this tool before make_api_request, providing clear usage context. However, it does not mention when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Unified tool for all Square API operations. Be sure to get types before calling. Available services: applepay, bankaccounts, bookingcustomattributes, bookings, cards, cashdrawers, catalog, checkout, customercustomattributes, customergroups, customersegments, customers, devices, disputes, events, giftcardactivities, giftcards, inventory, invoices, labor, locationcustomattributes, locations, loyalty, merchantcustomattributes, merchants, oauth, ordercustomattributes, orders, payments, payouts, refunds, sites, snippets, subscriptions, team, terminal, vendors, webhooksubscriptions.
| Name | Required | Description | Default |
|---|---|---|---|
| service | Yes | The Square API service category (e.g., 'catalog', 'payments') | |
| method | Yes | The API method to call (e.g., 'list', 'create') | |
| request | No | The request object for the API call. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states it is a unified tool and lists services. It does not disclose that it makes HTTP calls, requires authentication, can modify data, or has rate limits. Minimal behavioral context is provided.
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 extremely concise—two sentences with no superfluous words. It front-loads the core purpose and then lists services efficiently.
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?
Despite the tool's complexity (any API operation), the description lacks details on return values, how to structure the request object, or supported methods beyond 'list' and 'create' implied. Sibling tools exist but the description does not fully compensate for missing output schema or behavioral specifics.
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 coverage is 100%, but the description adds value by enumerating all available services, which is absent as enum constraints in the schema. This helps the agent select valid service values, going beyond the generic schema description.
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 states it is a unified tool for all Square API operations, clearly indicating its purpose as a general-purpose API caller. It distinguishes from sibling tools (get_service_info, get_type_info) by specifying it performs operations rather than information retrieval.
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 advises to 'get types before calling,' providing a prerequisite but not explicit when-to-use or when-not-to-use guidance. It implies this is the primary tool for API calls but does not contrast with alternatives beyond the mention of getting types.
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.
get_service_infoget_type_infomake_api_requestScored across 3 tools
Each tool has a distinct and clearly defined role in the workflow (service info, type info, API request), with no overlap in purpose.
All tool names follow a consistent verb_noun pattern (get_service_info, get_type_info, make_api_request), using snake_case throughout.
Three tools is minimal but appropriate for a unified API wrapper, as the tools cover the essential introspection and request workflow.
The tool set covers the full lifecycle: discover services, get type information, and make API requests. No obvious gaps for the intended purpose.
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
Connect AI to store orders, products and inventory with scoped access and human approvals.
Connect any AI agent to 1,000+ apps and 27,000+ actions through one remote MCP server (OAuth).
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
