How do I use mindbody-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mindbody-mcp show me classes for next Monday" 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.

mindbody-mcp

by NoBanks

Overview Schema Related Servers Score Discussions(1)

Python

Remote

mindbody-mcp

License: MIT Python MCP

MCP server for Mindbody, the vertical CRM for wellness, fitness, and beauty studios (yoga, gyms, salons, spas, training studios). 5 tools for AI agents to fetch client info, query class schedules, book clients into classes (env-gated), book appointments (env-gated), and complete shopping cart checkout (payment-gated).

As of May 2026, Mindbody does not ship an official MCP server. Multiple community attempts exist but are unofficial and self-hosted. This is the production-quality, install-ready Python rail. Shipped same-day as Mindbody's May 28 2026 Bilt Rewards distribution partnership.

The 5 tools

Tool	Purpose	Gated?
`get_client_info`	Fetch detailed client profile by ID	No, safe
`list_classes`	Query class schedules within a date range	No, safe
`book_client_to_class`	Enroll a client into a class	YES, `MINDBODY_ALLOW_BOOKINGS=true` required
`book_appointment`	Schedule a wellness or training appointment	YES, `MINDBODY_ALLOW_BOOKINGS=true` required
`checkout_shopping_cart`	Process payment for passes, services, or products	YES, `MINDBODY_ALLOW_CHECKOUT=true` required (separate flag, payment action)

Related MCP server: meetergo-mcp

Install

pip install mindbody-mcp

Configure

export MINDBODY_API_KEY="your-mindbody-developer-api-key"
export MINDBODY_SITE_ID="your-mindbody-site-id-integer"   # use -99 for sandbox
export MINDBODY_USER_TOKEN="your-mindbody-staff-user-token"   # optional but required for client + write actions
export MINDBODY_ALLOW_BOOKINGS="false"   # set to "true" to enable booking writes
export MINDBODY_ALLOW_CHECKOUT="false"   # set to "true" to enable payment processing (separate flag for safety)

Get an API key in your Mindbody Developer account. The user token is obtained via staff login flow; check the Mindbody Public API V6 docs for details.

Use with Claude Desktop

{
  "mcpServers": {
    "mindbody": {
      "command": "mindbody-mcp",
      "env": {
        "MINDBODY_API_KEY": "your-mindbody-developer-api-key",
        "MINDBODY_SITE_ID": "your-mindbody-site-id-integer",
        "MINDBODY_USER_TOKEN": "your-mindbody-staff-user-token",
        "MINDBODY_ALLOW_BOOKINGS": "false",
        "MINDBODY_ALLOW_CHECKOUT": "false"
      }
    }
  }
}

Restart Claude Desktop. The 5 Mindbody tools are now available.

Use case: AI front-desk + AI personal scheduler

Read-only flow (safe, default):

Call get_client_info(client_id) to load the caller's profile
Call list_classes(start_datetime, end_datetime) to surface available classes

Booking-enabled flow (after setting MINDBODY_ALLOW_BOOKINGS=true):

Call book_client_to_class(client_id, class_id) to enroll the caller
Call book_appointment(client_id, staff_id, session_type_id, start_datetime) for 1-on-1 sessions

Payment-enabled flow (after setting MINDBODY_ALLOW_CHECKOUT=true):

Call checkout_shopping_cart(client_id, item_id, item_type, quantity) to process payment

Safety note: two separate flags for write actions and payment

MINDBODY_ALLOW_BOOKINGS gates the two booking writes (class enrollment + appointment scheduling). Bookings create real reservations on the studio's calendar.
MINDBODY_ALLOW_CHECKOUT gates the payment action. Checkout actually charges the client's payment method. This is intentionally a SEPARATE flag so studio owners can enable AI agent bookings without also enabling AI agent purchasing. Two-step opt-in keeps real money out of an over-eager agent's reach by default.

Billing note

Since October 1, 2023, Mindbody charges $0.002 per API call in production environments. Operators should cache responses where possible to control billing overhead. This MCP does not cache responses; caching is the operator's responsibility per workflow.

Architecture

Public MIT-licensed wrapper around the Mindbody Public API V6
Async HTTP via httpx
pydantic v2 input validation
Three-header auth: Api-Key, SiteId, optional Authorization (user token)
Rate-limit aware (429 returns a clean error)
Write actions gated by env flag
Payment action gated by a SEPARATE env flag

Development

git clone https://github.com/NoBanks/mindbody-mcp.git
cd mindbody-mcp
pip install -e ".[dev]"
pytest

License

MIT. See LICENSE.

Author

Ryan Hammer (NoBanks). Solo founder + engineer. Built this and 15 other MCP servers as part of a sprint to expose AI agent rails for the products and platforms shipping daily. Built mindbody-mcp specifically as a same-day reply to Mindbody's May 28 2026 Bilt Rewards distribution partnership.

GitHub: @NoBanks
X/Twitter: @livingagentic
Site: livingagentic.me, nohumannearby.com

Open to AI engineering roles, contract or full-time, remote-only.

Available Tools

5 tools

book_appointmentA

Schedule a single wellness or training appointment for a client with a specific staff member at a specific time. GATED behind MINDBODY_ALLOW_BOOKINGS. Returns an error if not enabled.

ParametersJSON Schema

Name	Required	Description
`client_id`	Yes
`staff_id`	Yes	The staff member ID providing the service
`session_type_id`	Yes	The session type ID (massage, training, etc.)
`start_datetime`	Yes	ISO 8601 appointment start

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the gated feature and error condition but does not describe side effects, permissions needed, or expected success response. For a creation tool, more behavioral context is needed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise—two sentences that front-load the purpose and add a critical gating condition. No unnecessary words or redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 required parameters, no output schema, and no annotations, the description lacks important context such as what the tool returns on success, prerequisites (e.g., checking availability), and any rate limits. The gating information is helpful but incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 75% (client_id missing description). The tool description does not add any parameter-level information beyond what the schema provides. While the schema itself is reasonably descriptive, the missing client_id description is not compensated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool schedules a single wellness or training appointment for a client with a specific staff member at a specific time. It distinguishes itself from sibling tools like book_client_to_class (books a class) and checkout_shopping_cart (checkout process).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the gating condition (MINDBODY_ALLOW_BOOKINGS) and error return if not enabled, but does not provide explicit guidance on when to use this tool versus alternatives like book_client_to_class. The context is implied but not directly stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

book_client_to_classA

Enroll a client into an upcoming studio class. GATED behind MINDBODY_ALLOW_BOOKINGS env var. Returns an error if not enabled. This action results in a real reservation.

ParametersJSON Schema

Name	Required	Description	Default
`client_id`	Yes
`class_id`	Yes

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It states the gating condition and that the action results in a real reservation, but lacks details on idempotency, side effects, or whether the booking is irreversible.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with three sentences, each serving a purpose: stating the action, noting the gating condition, and clarifying the real-world impact. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 2 parameters and no output schema, the description covers the core purpose and a key constraint (env var gating), but omits details about parameter validation, failure modes like full classes, or expected behavior when successful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description fails to explain the parameters 'client_id' and 'class_id' beyond the implied meaning from the action. No details on format, source, or constraints are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action: enrolling a client into an upcoming studio class. The verb 'enroll' and the resource 'client into class' are explicit. It distinguishes itself from sibling tools like 'book_appointment' which likely books appointments, not classes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the tool is gated behind an environment variable and returns an error if not enabled, but does not provide explicit guidance on when to use this tool versus alternatives like 'book_appointment' or scenarios where it should not be used.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

checkout_shopping_cartA

Process purchases and complete payment checkout for passes, services, or products. GATED behind MINDBODY_ALLOW_CHECKOUT env var. This action TAKES PAYMENT from the client. Returns an error if not enabled.

ParametersJSON Schema

Name	Required	Description
`client_id`	Yes
`item_id`	Yes	The Mindbody item ID to purchase
`item_type`	Yes	Type of cart item
`quantity`	No

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavior. It states it takes payment and is gated, but lacks details on side effects (e.g., inventory changes, confirmation). It is reasonably transparent but not fully exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences effectively convey purpose, gate condition, and payment action. No wasted words; front-loaded with key intent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description explains the action (payment checkout) and a key prerequisite (env var). Missing details like confirmation or error scenarios, but sufficient for a payment trigger tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%, with descriptions for item_id and item_type. The description mentions 'passes, services, or products' which aligns with item_type enum, but does not add significant new meaning beyond schema. A baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it processes purchases and completes payment checkout for passes, services, or products. This contrasts well with sibling tools that focus on booking or client info, making its unique purpose evident.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly mentions being gated behind an environment variable and warns that it takes payment and returns an error if disabled. While it doesn't explicitly list when not to use, the context strongly implies it is for payment only, distinct from booking tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_client_infoB

Fetch detailed profile information for a Mindbody client. Used by AI front-desk agents to ground responses in customer history.

ParametersJSON Schema

Name	Required	Description	Default
`client_id`	Yes	The Mindbody client ID to query

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility. It only states 'Fetch detailed profile information', omitting any behavioral traits such as read-only nature, authentication needs, error behavior, or side effects. This is insufficient for a tool without annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the key action and resource, and no wasted words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Though the tool is simple with one parameter and no output schema, the description fails to explain what 'detailed profile information' includes, which is critical for an AI agent to ground responses. Without output schema or description of return data, the tool is under-specified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add any meaning beyond the schema's description of 'client_id'. No additional context is provided about the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Fetch' and the resource 'detailed profile information for a Mindbody client'. It also provides context ('Used by AI front-desk agents to ground responses in customer history'), which distinguishes it from sibling tools like book_appointment or list_classes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for grounding customer responses but does not explicitly state when to use this tool versus alternatives or provide exclusions. The sibling tools are functionally distinct, so the context is adequate but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_classesA

Query class schedules and active class lists within a date range. Used by AI scheduling agents to surface available classes to a caller.

ParametersJSON Schema

Name	Required	Description	Default
`start_datetime`	No	ISO 8601 start of window
`end_datetime`	No	ISO 8601 end of window

TDQS

A3.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description only says 'Query class schedules and active class lists within a date range' without details on return format, pagination, or behavior when parameters are omitted. This is insufficient for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, directly to the point, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description should clarify what the tool returns. It only mentions a query action but omits result format, limiting the agent's understanding of how to use the output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 both parameters. The description adds no additional meaning beyond the schema's ISO 8601 format specification.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it queries class schedules and active class lists within a date range, specifically for AI scheduling agents. This distinguishes it from sibling tools like book_appointment or book_client_to_class.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Context is provided for AI scheduling agents to surface available classes, implying when to use. However, it does not explicitly state when not to use or list alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv0.1.0May 29, 2026
- First observedbook_appointment
- First observedbook_client_to_class
- First observedcheckout_shopping_cart
- First observedget_client_info
- First observedlist_classes

Install Server

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TDQS

A3.8/5.0

Disambiguation5/5

Each tool targets a distinct action: booking an appointment, booking a class, processing payment, fetching client info, and listing classes. No functional overlap.

Naming Consistency5/5

All tools use consistent snake_case verb_noun pattern (book_appointment, book_client_to_class, checkout_shopping_cart, get_client_info, list_classes).

Tool Count5/5

5 tools is well within the ideal 3-15 range and covers core scheduling and client operations without feeling sparse or bloated.

Completeness3/5

Covers basic booking and client info but lacks cancel/modify appointments, staff listing, or class search beyond listing. Notable gaps for full lifecycle management.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

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