The Square Model Context Protocol Server enables AI assistants to interact with Square's API ecosystem. With this server, you can:
Execute API Operations: Use make_api_request to perform operations across Square services like catalog, payments, customers, orders, invoices, and inventory.
Discover API Methods: Use get_service_info to explore available methods within specific Square API services.
Understand API Requirements: Use get_type_info to learn parameter requirements for specific API methods.
Configure Flexibly: Set up with environment variables for access tokens, environment (sandbox/production), write restrictions, and API version.
Deploy Options: Run locally via npx or connect to a hosted remote version with OAuth authentication.
Integrate with AI: Works seamlessly with AI assistants like Goose and Claude Desktop.
Enables integration with Apple Pay through Square's API, allowing for payment processing via Apple's payment platform.
Provides access to Square's complete API ecosystem, enabling interactions with services such as catalog management, customer management, payment processing, order management, inventory tracking, appointment booking, gift cards, loyalty programs, and many other Square business services.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Square Model Context Protocol Server list my recent customers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This project follows the Model Context Protocol standard, allowing AI assistants to interact with Square's connect API.
Get up and running with the Square MCP server using 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-serverReplace YOUR_SQUARE_ACCESS_TOKEN with your actual Square access token. You can obtain your access token by following the guide at Square Access Tokens. You can also set environment variables before running the command.
Related MCP server: Fireblocks MCP Server
Square now offers a hosted remote MCP server at:
https://mcp.squareup.com/sseThe remote MCP is recommended as it uses OAuth authentication, allowing you to log in with your Square account directly without having to create or manage access tokens manually.
Environment Variable | Purpose | Example |
| Your Square API access token |
|
| Use Square sandbox environment |
|
| Use Square production environment |
|
| Restrict to read-only operations |
|
| Specify Square API version |
|
To configure the Square MCP Server with Goose:
To install the Square remote MCP in Goose, click this URL on a computer where Goose is installed:
Or copy and paste the URL into your browser's address bar.
# Automatic installation
npx square-mcp-server install
# Get URL for manual installation
npx square-mcp-server get-goose-urlThe install command automatically updates your Goose configuration.
For Claude Desktop integration, see the Model Context Protocol Quickstart Guide. Add this configuration to your claude_desktop_config.json:
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["mcp-remote", "https://mcp.squareup.com/sse"]
}
}
}This approach allows you to authenticate directly with your Square account credentials without needing to manage access tokens.
{
"mcpServers": {
"mcp_square_api": {
"command": "npx",
"args": ["square-mcp-server", "start"],
"env": {
"ACCESS_TOKEN": "YOUR_SQUARE_ACCESS_TOKEN",
"SANDBOX": "true"
}
}
}
}The Square MCP Server provides a streamlined set of tools for interacting with Square APIs:
Tool | Description | Primary Use |
| Discover methods available for a service | Exploration and discovery |
| Get detailed parameter requirements | Request preparation |
| Execute API calls to Square | Performing operations |
Square MCP Server provides access to Square's complete API ecosystem. Check out the Square API Documentation for detailed information about each service:
Service | Description |
| Apple Pay integration |
| Bank account management |
| Custom attributes for bookings |
| Appointment booking management |
| Payment card management |
| Cash drawer management |
| Catalog management (items, categories, etc.) |
| Checkout and payment processing |
| Custom attributes for customers |
| Customer grouping |
| Customer segmentation |
| Customer management |
| Square device management |
| Payment dispute handling |
| Event tracking |
| Gift card activity tracking |
| Gift card management |
| Inventory tracking |
| Invoice management |
| Workforce management |
| Custom attributes for locations |
| Location management |
| Loyalty program management |
| Custom attributes for merchants |
| Merchant account management |
| Authentication |
| Custom attributes for orders |
| Order management |
| Payment processing |
| Payout management |
| Refund management |
| Website integration |
| Square Online Code integration |
| Subscription management |
| Staff management |
| Square Terminal management |
| Supplier management |
| Event notifications |
For optimal interaction with the Square API through MCP:
Discover: Use get_service_info to explore available methods
get_service_info(service: "catalog")Understand: Use get_type_info to learn parameter requirements
get_type_info(service: "catalog", method: "list")Execute: Use make_api_request to perform the operation
make_api_request(service: "catalog", method: "list", request: {})The MCP Inspector provides a visual interface for testing:
# Build the project
npm run build
# Start the inspector with the Square MCP Server
npx @modelcontextprotocol/inspector node dist/index.js startClone the repository
Install dependencies: npm install
Start development mode: npm run watch
Run the server: node dist/index.js start
Test your changes using the MCP Inspector
This repository is auto-generated from Square's OpenAPI Specification. While contributions are welcome, please note that changes will need to be incorporated into the generator that produces this code. Please open an issue to discuss proposed changes before submitting a pull request.
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. Dates show when Glama detected each change.
get_service_infoget_type_infomake_api_requestEach 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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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