AB Tasty MCP Server

A Model Context Protocol (MCP) server that integrates AB Tasty's Feature Experimentation & Rollouts platform with AI assistants. This server provides tools for feature flag management, campaign retrieval, and guided SDK integration through the MCP protocol.

Features

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- Decision API Integration: Retrieve campaigns and feature flags for visitors
- decision_api_get_campaigns: Get all campaigns for a visitor with context
- decision_api_get_campaign: Get a specific campaign by ID for a visitor
- decision_api_get_flags: Get all feature flags for a visitor
- decision_api_activate_campaign: Activate a campaign for a visitor

- Resource Loader API: Extract and analyze campaign configurations (v0.2.0+)
- resource_loader_api_load_webexp_resources: Load Web Experimentation & Personalization resources (use with we_resource_extractor prompt)
- resource_loader_api_load_featexp_resources: Load Feature Experimentation & Rollout resources (use with fear_resource_extractor prompt)
- _Note: In v0.2.0, the Resource Loader API was split into platform-specific tools for better clarity_

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- Quick Start Guides: Interactive prompts for AB Tasty SDK installation
- Node.js SDK installation guide
- Resource Extractors: AI-powered resource configuration generators
- we_resource_extractor: Converts natural language campaign briefs into Web Experimentation resource configurations
- fear_resource_extractor: Converts natural language campaign briefs into Feature Experimentation resource configurations
- These prompts generate the JSON needed for the Resource Loader API tools

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- Documentation: Access to AB Tasty Feature Experimentation & Rollout documentation directly through MCP

Prerequisites

- Node.js 18+
- npm or yarn
- AB Tasty credentials:
- Environment ID
- API Key
- (Optional) Account ID and token for Resource Loader features for Web Experimentation

Installation

1. Clone the repository:

``bash
git clone https://github.com/flagship-io/mcp-server.git
cd mcp-server
`

2. Install dependencies:

`bash
yarn install
`

3. Build the project:

`bash
yarn build
`

Usage

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The easiest way to use the MCP server is through npx without any installation:

`json
{
"mcpServers": {
"ABTasty": {
"command": "npx",
"args": [
"-y",
"@abtasty/mcp-server",
"--fear-env-id",
"YOUR_FEAR_ENV_ID",
"--fear-api-key",
"YOUR_FEAR_API_KEY",
"--resource-loader-fear-account-id",
"YOUR_FEAR_ACCOUNT_ID",
"--resource-loader-fear-account-environment-id",
"YOUR_FEAR_ACCOUNT_ENVIRONMENT_ID",
"--resource-loader-fear-rca-token",
"YOUR_FEAR_RCA_TOKEN",
"--resource-loader-we-account-id",
"YOUR_WE_ACCOUNT_ID",
"--resource-loader-we-token",
"YOUR_WE_TOKEN"
]
}
}
}
`

Replace the placeholder values with your actual credentials:

- YOUR_FEAR_ENV_ID: Your AB Tasty Feature Experimentation account environment ID
- YOUR_FEAR_API_KEY: Your AB Tasty Feature Experimentation API key
- YOUR_FEAR_ACCOUNT_ID: Your AB Tasty Feature Experimentation Account ID
- YOUR_FEAR_ACCOUNT_ENVIRONMENT_ID: Your AB Tasty Feature Experimentation account Environment ID
- YOUR_FEAR_RCA_TOKEN: Your AB Tasty Feature Experimentation RCA Token
- YOUR_WE_ACCOUNT_ID: Your AB Tasty Web Experimentation Account ID
- YOUR_WE_TOKEN: Your AB Tasty Web Experimentation token

Alternative: Environment Variables

You can also set credentials via environment variables instead of command-line arguments:

`bash


Feature Experimentation & Rollout

export FEAR_ENV_ID="your_fear_env_id"
export FEAR_API_KEY="your_fear_api_key"

Resource Loader - Feature Experimentation & Rollout

export RESOURCE_LOADER_FEAR_ACCOUNT_ID="your_fear_account_id"
export RESOURCE_LOADER_FEAR_ACCOUNT_ENVIRONMENT_ID="your_fear_account_environment_id"
export RESOURCE_LOADER_FEAR_RCA_TOKEN="your_fear_rca_token"

Resource Loader - Web Experimentation & Personalization

export RESOURCE_LOADER_WE_ACCOUNT_ID="your_we_account_id"
export RESOURCE_LOADER_WE_TOKEN="your_we_token"
`

Then use a simpler npx command:

`json
{
"mcpServers": {
"ABTasty": {
"command": "npx",
"args": ["-y", "@abtasty/mcp-server"]
}
}
}
`

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Start the server on the default port (3000):

`bash
yarn start
`

Or specify a custom port:

`bash
PORT=8080 yarn start
`

The server will be available at http://localhost:3000/mcp (or your custom port).

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The server accepts configuration through HTTP headers for each session:

#### Feature Experimentation & Rollout (FEAR) Headers:

- x-fear-env-id: Your AB Tasty Feature Experimentation & Rollout environment ID
- x-fear-api-key: Your AB Tasty Feature Experimentation & Rollout API key

#### Resource Loader Headers (optional):

For Web Experimentation & Personalization:

- x-resource-loader-we-account-id: Your AB Tasty Web Experimentation account ID
- x-resource-loader-we-token: Your Web Experimentation resource loader authentication token

For Feature Experimentation & Rollout:

- x-resource-loader-fear-account-id: Your AB Tasty Feature Experimentation & Rollout account ID
- x-resource-loader-fear-account-environment-id: Your AB Tasty Feature Experimentation & Rollout environment ID
- x-resource-loader-fear-rca-token: Your Feature Experimentation & Rollout (Remote Control API) resource loader authentication token

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The server supports two modes of operation:

#### 1. Stdio Mode (for Claude Desktop and similar clients)

Use the npx command shown above for stdio-based MCP clients like Claude Desktop.

#### 2. HTTP Mode (Streamable HTTP transport)

For HTTP-based clients, the server uses the Streamable HTTP transport protocol. Configure your MCP client to connect to the server endpoint:

`json
{
"mcpServers": {
"ABTasty": {
"url": "http://localhost:3000/mcp",
"headers": {
"x-fear-env-id": "your_env_id",
"x-fear-api-key": "your_api_key"
}
}
}
}
`

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Once connected through an MCP client, you can use the available tools:

Get campaigns for a visitor:

`typescript
decision_api_get_campaigns({
visitor_id: "user123",
context: {
age: 25,
country: "US"
},
trigger_hit: true
});
`

Get feature flags:

`typescript
decision_api_get_flags({
visitor_id: "user123",
context: {
age: 25,
country: "US"
},
trigger_hit: false
});
`

Create and load Web Experimentation resources:

Step 1: Use the we_resource_extractor prompt to convert your campaign brief into a resource configuration:

`text
User: Create an A/B test on https://example.com/pricing.
Show a red button for 50% of traffic.

Prompt Output:
{
"needs_clarification": false,
"questions": [],
"resources": [
{
"type": "campaign",
"$_ref": "c1",
"action": "create",
"payload": {...}
}
]
}
`

Step 2: Load the resources using the tool:

`typescript
resource_loader_api_load_webexp_resources({
resourceLoaderContent: {
"needs_clarification": false,
"questions": [],
"resources": [...]
},
dryrun: false,
});
`

Create and load Feature Experimentation resources:

Step 1: Use the fear_resource_extractor prompt to convert your campaign brief:

`text
User: Create a feature flag 'new-checkout' for 25% of users

Prompt Output:
{
"version": 1,
"needs_clarification": false,
"questions": [],
"resources": [
{
"type": "project",
"$_ref": "p1",
"action": "create",
"payload": {...}
},
{
"type": "campaign",
"$_ref": "c1",
"action": "create",
"payload": {...}
}
]
}
`

Step 2: Load the resources using the tool:

`typescript
resource_loader_api_load_featexp_resources({
resourceLoaderContent: {
"version": 1,
"needs_clarification": false,
"questions": [],
"resources": [...]
},
dryrun: false,
});
`

Development

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`
├── src/
│ ├── index.ts # Main HTTP server entry point
│ ├── cli.ts # CLI entry point for stdio mode
│ ├── tools/ # MCP tool implementations
│ │ ├── decision-api.ts # Feature flag tools
│ │ └── resource-loader-api.ts
│ ├── prompts/ # Interactive prompt definitions
│ │ ├── quickstart-guide.ts
│ │ └── resource-loader-prompts.ts
│ └── resources/ # Resource providers
│ └── documentation.ts
├── helpers/ # Utility functions
│ ├── abtasty-fear.ts # AB Tasty Feature Experimentation SDK wrapper
│ └── resource-loader.ts # Campaign extraction utilities
├── types/ # TypeScript type definitions
├── assistant-prompts/ # Markdown prompt templates
└── build/ # Compiled output
`

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`bash


Generate prompt files

yarn generate:prompts

Build the project

yarn build

Build and run

yarn dev
`

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The build process:

1. Generates TypeScript files from markdown prompts
2. Bundles the application with esbuild
3. Copies assistant prompt files to the build directory

`bash
yarn build
`

Architecture

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The server maintains isolated sessions for each client connection:

- Each session has its own credentials (provided via headers)
- Sessions are identified by unique UUIDs
- Sessions are cleaned up when connections close

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Uses the Streamable HTTP transport from the MCP SDK:

- Supports JSON-RPC 2.0 over HTTP
- Session-based connection management
- Automatic session initialization and cleanup

Security Notes

- Credentials are session-scoped and not shared between sessions
- Always use HTTPS in production environments
- Store credentials securely and never commit them to version control

API Documentation

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#### decision_api_get_campaigns

Retrieves all campaigns for a visitor.

Parameters:

- visitor_id (string): Unique visitor identifier
- context (object): Key-value pairs for targeting (optional)
- trigger_hit (boolean): Send analytics hit (default: false)

Returns: Array of campaigns with variations and modifications

#### decision_api_get_campaign

Retrieves a specific campaign by its ID for a visitor.

Parameters:

- visitor_id (string): Unique visitor identifier
- campaign_id (string): Campaign ID to retrieve
- context (object): Key-value pairs for targeting (optional)
- trigger_hit (boolean): Send analytics hit (default: false)

Returns: Campaign with variation and modifications

#### decision_api_get_flags

Retrieves all feature flags for a visitor.

Parameters:

- visitor_id (string): Unique visitor identifier
- context (object): Key-value pairs for targeting (optional)
- trigger_hit (boolean): Send analytics hit (default: false)

Returns: Object with flag keys and their values

#### decision_api_activate_campaign

Activates a campaign for a visitor.

Parameters:

- visitor_id (string): Unique visitor identifier
- variation_group_id (string): Variation group ID to activate
- variation_id (string): Variation ID to activate

Returns: Activation confirmation

#### resource_loader_api_load_webexp_resources

Loads Web Experimentation & Personalization resources via the Resource Loader API.

Workflow: Use the we_resource_extractor prompt to generate the resourceLoaderContent from a natural language campaign brief, then pass it to this tool.

Parameters:

- resourceLoaderContent (object): JSON containing resource loader content with needs_clarification, questions, and resources array
- dryrun (boolean): Whether to simulate the request without sending it

Returns: Loaded resources results

#### resource_loader_api_load_featexp_resources

Loads Feature Experimentation & Rollout resources via the Resource Loader API.

Workflow: Use the fear_resource_extractor prompt to generate the resourceLoaderContent from a natural language campaign brief, then pass it to this tool.

Parameters:

- resourceLoaderContent (object): JSON containing resource loader content with version, needs_clarification, questions, and resources array
- dryrun (boolean): Whether to simulate the request without sending it

Returns: Loaded resources results

Troubleshooting

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- Check if port 3000 is already in use
- Verify Node.js version (18+ required)
- Ensure dependencies are installed: yarn install`

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- Verify your environment ID and API key are correct
- Check that headers are properly set in your MCP client configuration
- Ensure credentials have the necessary permissions in AB Tasty

License

MIT License

Support

For issues related to:

- This MCP server: Open an issue on GitHub
- AB Tasty platform: Contact AB Tasty support

Resources

- MCP protocol: See Model Context Protocol documentation
- AB Tasty SDK: See AB Tasty SDK