Dieses Projekt folgt dem Model Context Protocol- Standard und ermöglicht KI-Assistenten die Interaktion mit der Connect-API von Square.
Starten Sie den Square MCP-Server mit npx:
# Basic startup
npx square-mcp-server start
# With environment configuration
ACCESS_TOKEN=YOUR_SQUARE_ACCESS_TOKEN SANDBOX=true npx square-mcp-server start
# local runs
npx /path/to/project/square-mcp-serverErsetzen Sie YOUR_SQUARE_ACCESS_TOKEN durch Ihren tatsächlichen Square-Zugriffstoken. Sie erhalten Ihren Zugriffstoken gemäß der Anleitung unter Square Access Tokens . Sie können vor der Ausführung des Befehls auch Umgebungsvariablen festlegen.
Related MCP server: AI-Assisted CRM MCP Server
Square bietet jetzt einen gehosteten Remote-MCP-Server unter:
https://mcp.squareup.com/sseDas Remote-MCP wird empfohlen, da es die OAuth-Authentifizierung verwendet und Ihnen die direkte Anmeldung mit Ihrem Square-Konto ermöglicht, ohne Zugriffstoken manuell erstellen oder verwalten zu müssen.
Umgebungsvariable | Zweck | Beispiel |
| Ihr Square API-Zugriffstoken |
|
| Verwenden Sie die Square-Sandbox-Umgebung |
|
| Verwenden Sie die Square-Produktionsumgebung |
|
| Auf schreibgeschützte Vorgänge beschränken |
|
| Geben Sie die Square-API-Version an |
|
So konfigurieren Sie den Square MCP-Server mit Goose :
Um das Square Remote MCP in Goose zu installieren, klicken Sie auf einem Computer, auf dem Goose installiert ist, auf diese URL:
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
Oder kopieren Sie die URL und fügen Sie sie in die Adressleiste Ihres Browsers ein.
# Automatic installation
npx square-mcp-server install
# Get URL for manual installation
npx square-mcp-server get-goose-urlDer install aktualisiert Ihre Goose-Konfiguration automatisch.
Informationen zur Claude Desktop-Integration finden Sie im Model Context Protocol Quickstart Guide . Fügen Sie diese Konfiguration zu Ihrer claude_desktop_config.json hinzu:
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["mcp-remote", "https://mcp.squareup.com/sse"]
}
}
}Dieser Ansatz ermöglicht Ihnen die direkte Authentifizierung mit den Anmeldeinformationen Ihres Square-Kontos, ohne dass Sie Zugriffstoken verwalten müssen.
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["square-mcp-server", "start"],
"env": {
"ACCESS_TOKEN": "YOUR_SQUARE_ACCESS_TOKEN",
"SANDBOX": "true"
}
}
}
}Der Square MCP-Server bietet einen optimierten Satz von Tools für die Interaktion mit Square-APIs:
Werkzeug | Beschreibung | Primäre Verwendung |
| Entdecken Sie die für einen Dienst verfügbaren Methoden | Erkundung und Entdeckung |
| Erhalten Sie detaillierte Parameteranforderungen | Anfragevorbereitung |
| Führen Sie API-Aufrufe an Square aus | Durchführen von Operationen |
Der Square MCP Server bietet Zugriff auf das komplette API-Ökosystem von Square. Detaillierte Informationen zu den einzelnen Diensten finden Sie in der Square API-Dokumentation :
Service | Beschreibung |
| Apple Pay-Integration |
| Bankkontoverwaltung |
| Benutzerdefinierte Attribute für Buchungen |
| Terminbuchungsverwaltung |
| Zahlungskartenverwaltung |
| Kassenladenverwaltung |
| Katalogverwaltung (Artikel, Kategorien usw.) |
| Kaufabwicklung und Zahlungsabwicklung |
| Benutzerdefinierte Attribute für Kunden |
| Kundengruppierung |
| Kundensegmentierung |
| Kundenmanagement |
| Square-Geräteverwaltung |
| Bearbeitung von Zahlungsstreitigkeiten |
| Ereignisverfolgung |
| Tracking der Geschenkkartenaktivität |
| Geschenkkartenverwaltung |
| Bestandsverfolgung |
| Rechnungsverwaltung |
| Personalmanagement |
| Benutzerdefinierte Attribute für Standorte |
| Standortverwaltung |
| Verwaltung von Treueprogrammen |
| Benutzerdefinierte Attribute für Händler |
| Händlerkontoverwaltung |
| Authentifizierung |
| Benutzerdefinierte Attribute für Bestellungen |
| Auftragsverwaltung |
| Zahlungsabwicklung |
| Auszahlungsmanagement |
| Rückerstattungsmanagement |
| Website-Integration |
| Square Online Code-Integration |
| Abonnementverwaltung |
| Personalmanagement |
| Square Terminal-Verwaltung |
| Lieferantenmanagement |
| Ereignisbenachrichtigungen |
Für eine optimale Interaktion mit der Square-API über MCP:
Entdecken : Verwenden Sie get_service_info , um verfügbare Methoden zu erkunden
get_service_info(service: "catalog")Verstehen : Verwenden Sie get_type_info , um Parameteranforderungen zu erfahren
get_type_info(service: "catalog", method: "list")Ausführen : Verwenden Sie make_api_request , um den Vorgang auszuführen
make_api_request(service: "catalog", method: "list", request: {})Der MCP Inspector bietet eine visuelle Schnittstelle zum Testen:
# Build the project
npm run build
# Start the inspector with the Square MCP Server
npx @modelcontextprotocol/inspector node dist/index.js startKlonen Sie das Repository
Abhängigkeiten installieren: npm install
Entwicklungsmodus starten: npm run watch
Führen Sie den Server aus: node dist/index.js start
Testen Sie Ihre Änderungen mit dem MCP Inspector
Dieses Repository wird automatisch aus der OpenAPI-Spezifikation von Square generiert. Beiträge sind willkommen, beachten Sie jedoch, dass Änderungen in den Generator, der diesen Code erzeugt, integriert werden müssen. Bitte eröffnen Sie ein Issue, um die vorgeschlagenen Änderungen zu besprechen, bevor Sie einen Pull Request einreichen.
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
