Unleash MCP Server

A purpose-driven Model Context Protocol (MCP) server for managing Unleash feature flags. This server enables LLM-powered coding assistants to create and manage feature flags following Unleash best practices.

Overview

This MCP server provides tools that integrate with the Unleash Admin API. It evaluates code changes to decide when a feature flag is needed, creates flags with proper validation and typing, and detects existing flags to prevent duplicates or encourage reuse. The server streams progress for visibility during operations, handles errors gracefully with helpful hints, and follows best practices from the Unleash documentation.

Every tool falls into one of these use cases:

See the Tool reference section for detailed parameters and output for each tool.

Workflows

The sections below walk through each use case. For agent-specific prompt examples and setup, see the dedicated integration guides for Claude Code, Cursor, GitHub Copilot, Kiro, and OpenCode.

Get started

You have two ways to run the Unleash MCP server:

• Local MCP server: Runs inside your LLM client on your machine. Use this setup if your organization allows installing MCP servers on developer laptops. See Run the MCP server locally.
• Remote MCP server Beta: Hosted by your Unleash instance. Use this setup if developers cannot install local MCP servers, or if you want a single, managed entry point for all clients.

Run the MCP server locally

This section covers the different ways to install and run the Unleash MCP server locally. You can either follow a setup for agents (such as Claude Code and Codex), run the MCP as a standalone process using npx, or use a local development setup.

Prerequisites

Before you can run the server locally, you need the following:

• Node.js 18 or higher
• Yarn package manager or npm
• An Unleash instance (hosted or self-hosted)
• A personal access token with permissions to create feature flags

Agent setup

You can add the MCP server directly to Claude Code or Codex. Agent configurations are path-specific. You must run the following command from the root directory of the project where you want to use the MCP.

For Claude Code:

$
claude mcp add unleash \
>
    --env UNLEASH_BASE_URL={{your-instance-url}} \
>
    --env UNLEASH_PAT={{your-personal-access-token}} \
>
    -- npx -y @unleash/mcp@latest --log-level error

Check out the dedicated Claude Code integration guide for additional guidance and best practices.

For Codex:

$
codex mcp add unleash \
>
    --env UNLEASH_BASE_URL={{your-instance-url}} \
>
    --env UNLEASH_PAT={{your-personal-access-token}} \
>
    -- npx -y @unleash/mcp@latest --log-level error

For other agents, see the dedicated integration guides:

• Cursor
• GitHub Copilot (VS Code and Copilot CLI)
• Kiro

Quickstart with npx

You can run the MCP server as a standalone process without cloning the repository using npx. Provide configuration through environment variables or a local .env file in the directory where you run the command:

$
UNLEASH_BASE_URL={{your-instance-url}} \
>
UNLEASH_PAT={{your-personal-access-token}} \
>
UNLEASH_DEFAULT_PROJECT={{default_project_id}} \
>
npx unleash-mcp --log-level debug

The CLI supports the same flags as the local build (for example, --dry-run, --log-level).

Local development setup

Follow these steps to set up the project for local development.

1. Install dependencies

Clone the repository and install dependencies using Yarn.

$
git clone https://github.com/Unleash/unleash-mcp.git
$
cd unleash-mcp
$
yarn install

2. Configure environment variables

Copy .env.example to .env and fill in your Unleash credentials:

$
cp .env.example .env

Edit .env:

1
UNLEASH_BASE_URL={{your-instance-url}}
2
UNLEASH_PAT={{your-personal-access-token}}
3
UNLEASH_DEFAULT_PROJECT={{default_project_id}}  # Optional: the project the MCP should use by default

3. Build the project

$
yarn build

Output will be in the dist/ directory.

4. (Optional) Run checks

Run type checking, linting, and tests:

$
# Type checking and linting
$
yarn lint
$
$
# Run tests (the Vitest framework is configured, but no test suites yet)
$
yarn test

Running the server

Development mode with hot reload

$
yarn dev

Production mode

$
node dist/index.js

With CLI flags

$
# Dry run mode (simulates API calls without actually creating flags)
$
node dist/index.js --dry-run
$
$
# Custom log level
$
node dist/index.js --log-level debug
$
$
# Combine flags
$
node dist/index.js --dry-run --log-level debug

Remote MCP server

The remote MCP server lets LLM clients connect to a Streamable HTTP MCP server hosted by your Unleash instance, without installing anything locally. This is useful when developers cannot run MCP servers on their laptops, or when you want to manage MCP access centrally.

When enabled, your Unleash instance exposes an MCP endpoint at:

https://<your-instance-url>/api/admin/mcp

The remote MCP server exposes the same tools as the local server.

Enable the remote MCP server

To enable the remote MCP server on your instance:

Authentication

The remote MCP server authenticates requests using standard Unleash personal access tokens (PATs). Once the remote MCP server is enabled, users can exchange their current login session for a PAT that is valid for 24 hours, and the MCP client uses that token to call the endpoint.

Connect a client

Once the remote MCP server is enabled, point your MCP client at the endpoint shown in Admin settings > Remote MCP server and complete the authentication flow in your browser to issue a PAT for the client.

Tool reference

This section describes each of the core tools in detail, including its purpose, parameters, and output.

Create flag

The create_flag tool creates a new feature flag in Unleash with comprehensive validation and progress tracking.

When to use

Use this tool when you have already determined that a feature flag is required (for example, after running evaluate_change) and you are ready to create it with the correct type and metadata.

Parameters

The tool accepts the following parameters:

• name (required): Unique feature flag name within the project.
• type (required): Feature flag type indicating lifecycle and intent.
  • release: Gradual feature rollouts to users.
  • experiment: A/B tests and experiments.
  • operational: System behavior and operational toggles.
  • kill-switch: Emergency shutdowns or circuit breakers.
  • permission: Control feature access based on user roles or entitlements.
• description (required): Clear explanation of what the flag controls and why it exists.
• projectId (optional): Target project (defaults to UNLEASH_DEFAULT_PROJECT).
• impressionData (optional): Enable analytics tracking (defaults to false).

Usage example

Agent prompt

Use create_flag with:
- name: "new-checkout-flow"
- type: "release"
- description: "Gradual rollout of the redesigned checkout experience"
- projectId: "ecommerce"

Tool payload

1
{
2
  "name": "new-checkout-flow",
3
  "type": "release",
4
  "description": "Gradual rollout of the redesigned checkout experience with improved conversion tracking",
5
  "projectId": "ecommerce",
6
  "impressionData": true
7
}

Tool output

On success, the tool returns a JSON object containing the new feature flag’s URL in the Unleash Admin UI, an MCP resource link for programmatic access, creation timestamp, and configuration details.

Evaluate change

The evaluate_change tool evaluates whether a code change should be behind a feature flag. It examines the structure, context, and potential risk of the change and returns a recommendation with an explanation and next steps.

When to use

Use evaluate_change at the beginning of a feature or modification when you want to understand whether the work requires a feature flag. This tool is also helpful when you are unsure which flag type to use or want guidance on rollout planning.

How it works

The tool returns detailed, markdown-formatted guidance for the LLM assistant based on Unleash best practices.

The guidance includes:

• Parent flag detection: Checks if code is already protected by existing flags.
• Risk assessment: Analyzes code patterns to identify risky operations.
• Code type evaluation: Classifies the change (for example, test, config, feature, or bug fix).
• Recommendation: Suggests whether to create a flag, use an existing flag, or skip the flag.
• Next actions: Provides specific instructions on what to do next.

When evaluate_change determines a flag is needed, it provides explicit instructions to:

1. Call create_flag tool to create the feature flag.
2. Call wrap_change tool to get language-specific code wrapping guidance.
3. Implement the wrapped code following the detected patterns.

The evaluation process

The tool follows a clear evaluation process:

Step 1: Gather code changes (git diff, read files)
        ↓
Step 2: Check for parent flags (avoiding nesting)
        ↓
Step 3: Assess code type (test? config? feature?)
        ↓
Step 4: Evaluate risk (auth? payments? API changes?)
        ↓
Step 5: Calculate risk score
        ↓
Step 6: Make recommendation
        ↓
Step 7: Take action (create flag or proceed without)

Risk assessment

The tool uses language-agnostic patterns to score risk:

• Critical risk (Score +5): For example, auth, payments, security, and database operations.
• High risk (Score +3): For example, API changes, external services, or new classes.
• Medium risk (Score +2): For example, async operations or state management.
• Low risk (Score +1): For example, bug fixes, refactors, or small changes.

Parent flag detection

The tool looks for common patterns across languages, such as:

• Conditionals: if (isEnabled('flag')), if client.is_enabled('flag'):
• Assignments: const enabled = useFlag('flag')
• Hooks: const enabled = useFlag('flag') → {enabled && <Component />}
• Guards: if (!isEnabled('flag')) return;
• Wrappers: withFeatureFlag('flag', () => {...})

Parameters

All parameters are optional, but more context leads to better recommendations:

• repository (string): Repository name or path.
• branch (string): Current branch name.
• files (array): List of files being changed.
• description (string): Description of the change.
• riskLevel (enum): low, medium, high, or critical, as assessed by the user.
• codeContext (string): Surrounding code for parent flag detection.

Usage example

Agent prompt

Simple usage where you let the agent gather context:

Use evaluate_change to help me determine if I need a feature flag

Explicit instructions:

Use evaluate_change with:
- description: "Add Stripe payment processing"
- riskLevel: "high"

Tool payload

1
{
2
  "repository": "my-app",
3
  "branch": "feature/stripe-integration",
4
  "files": ["src/payments/stripe.ts"],
5
  "description": "Add Stripe payment processing",
6
  "riskLevel": "high",
7
  "codeContext": "surrounding code for parent flag detection"
8
}

Tool output

Returns a JSON object with the evaluation result, including a needsFlag boolean, a recommendation (e.g., “create_new”), a suggested flag name, risk level, and a detailed explanation.

1
{
2
  "needsFlag": true,
3
  "reason": "new_feature",
4
  "recommendation": "create_new",
5
  "suggestedFlag": "stripe-payment-integration",
6
  "riskLevel": "critical",
7
  "riskScore": 5,
8
  "explanation": "This change integrates Stripe payments, which is critical risk...",
9
  "confidence": 0.9
10
}

Detect flag

The detect_flag tool finds existing feature flags in the codebase so you can reuse them instead of creating duplicates. This tool is automatically integrated into the evaluate_change workflow but can also be used manually.

When to use

Use this tool before creating a new feature flag or during code evaluation to check for existing flags that might already cover your use case. This helps prevent flag duplication.

How it works

The tool returns comprehensive search instructions and uses multiple detection strategies:

• File-based detection: Search in files you’re modifying for existing flags.
• Git history analysis: Look for recently added flags in commit history.
• Semantic name matching: Match descriptions to existing flag names.
• Code context analysis: Inspect code around the change.

The tool then follows a scoring process:

Step 1: Execute file-based search (grep for flag patterns in target files)
        ↓
Step 2: Search git history for recent flag additions
        ↓
Step 3: Perform semantic matching (description → flag names)
        ↓
Step 4: Analyze code context (if provided)
        ↓
Step 5: Combine scores from all methods
        ↓
Step 6: Return best candidate with confidence score

Confidence levels

The tool returns candidates with confidence scores:

• High ≥0.7: Strong match; reuse is recommended.
• Medium 0.4-0.7: Possible match; review manually.
• Low <0.4: Weak match; likely create a new flag.

Parameters

• description (required): Description of the change or feature. For example, "payment processing with Stripe", "new checkout flow".
• files (optional): Files being modified. For example, ["src/payments/stripe.ts", "src/checkout/flow.ts"].
• codeContext (optional): Nearby code to scan for flags.

Usage example

Agent prompt

Check for existing flags before creating a flag:

Use detect_flag with description "payment processing with Stripe"

Integrated automatically in evaluation:

Use evaluate_change - automatically searches for existing flags

Tool payload

1
{
2
  "description": "payment processing with Stripe",
3
  "files": ["src/payments/stripe.ts"]
4
}

Tool output

Returns a JSON object indicating if a flag was found. If flagFound is true, it includes a candidate object with the flag’s name, location, confidence score, and the reason for the match.

Match found:

1
{
2
  "flagFound": true,
3
  "candidate": {
4
    "name": "stripe-payment-integration",
5
    "location": "src/payments/stripe.ts:42",
6
    "context": "if (client.isEnabled('stripe-payment-integration')) {",
7
    "confidence": 0.85,
8
    "reasoning": "Found in same file you're modifying, added 2 days ago",
9
    "detectionMethod": "file-based"
10
  }
11
}

No match found:

1
{
2
  "flagFound": false,
3
  "candidate": null
4
}

Wrap change

The tool wrap_change generates language-specific code snippets and guidance for wrapping code with feature flags. It helps LLMs and developers follow existing patterns in the codebase and use flags correctly.

When to use

Use this tool after you have created a feature flag (with create_flag) and need to implement it in your code. It’s especially useful when you want to ensure you are following existing codebase patterns or need framework-specific examples (e.g., React, Django).

How it works

This tool is the final step in the evaluate_change → create_flag → wrap_change workflow.

The tool provides the following guidance in its response:

1. Search instructions: Step-by-step guide for finding existing flag patterns in your codebase using grep.
2. Pattern detection: Identifies common patterns (for example, imports, client variable names, method names, or wrapping styles).
3. Default templates: Fallback code snippets if no patterns are found.
4. Framework-specific examples: Specialized patterns for React, Express, Django, and others.
5. Multiple patterns: If-blocks, guard clauses, hooks, decorators, middleware, and more.

Supported languages and frameworks:

• TypeScript/JavaScript: Node.js, React Hooks, Express middleware.
• Python: FastAPI, Django, Flask decorators.
• Go: Standard if-blocks, HTTP middleware.
• Ruby: Rails controllers.
• PHP: Laravel controllers.
• C#: .NET/ASP.NET controllers.
• Java: Spring Boot.
• Rust: Actix/Rocket handlers.

Parameters

• flagName (required): Feature flag name to wrap the code with. For example: "new-checkout-flow", or "stripe-integration".
• language (optional): Programming language (auto-detected from fileName if not provided). Supported: typescript, javascript, python, go, ruby, php, csharp, java, rust
• fileName (optional): File name being modified (helps detect language), For example: "checkout.ts", "payment.py", or "handler.go".
• codeContext (optional): Surrounding code to help detect existing patterns.
• frameworkHint (optional): Framework for specialized templates. For example, "React", "Express", "Django", "Rails", or "Spring Boot".

Usage example

Agent prompt

Use wrap_change with:
- flagName: "new-checkout-flow"
- fileName: "src/components/checkout.ts"
- frameworkHint: "React"

Tool payload

1
{
2
  "flagName": "new-checkout-flow",
3
  "fileName": "checkout.ts",
4
  "frameworkHint": "React"
5
}

Tool output

Returns a comprehensive, markdown-formatted string that guides the user on how to wrap their code. This includes a quickstart, search instructions, wrapping instructions with placeholders, all available templates for the language, and links to SDK documentation.

1
# Feature Flag Wrapping Guide: "new-checkout-flow"
2
3
**Language:** TypeScript
4
**Framework:** React
5
6
## Quick Start
7
[Recommended pattern with import and usage]
8
9
## How to Search for Existing Flag Patterns
10
[Step-by-step Grep instructions]
11
12
## How to Wrap Code with Feature Flag
13
[Wrapping instructions with examples]
14
15
## All Available Templates
16
[If-block, guard clause, hooks, ternary, etc.]

Architecture

The server follows a focused, purpose-driven design.

Structure

src/
├── index.ts                     # Main server entry point
├── config.ts                    # Configuration loading and validation
├── context.ts                   # Shared runtime context
├── unleash/
│   └── client.ts                # Unleash Admin API client
├── tools/
│   ├── createFlag.ts            # create_flag tool
│   ├── evaluateChange.ts        # evaluate_change tool
│   ├── detectFlag.ts            # detect_flag tool
│   └── wrapChange.ts            # wrap_change tool
├── prompts/
│   └── promptBuilder.ts         # Markdown formatting utilities
├── evaluation/
│   ├── riskPatterns.ts          # Risk assessment patterns
│   └── flagDetectionPatterns.ts # Parent flag detection patterns
├── detection/
│   ├── flagDiscovery.ts         # Flag discovery strategies
│   └── flagScoring.ts           # Scoring and ranking logic
├── knowledge/
│   └── unleashBestPractices.ts  # Best practices knowledge base
├── templates/
│   ├── languages.ts             # Language detection and metadata
│   ├── wrapperTemplates.ts      # Code wrapping templates
│   └── searchGuidance.ts        # Pattern search instructions
└── utils/
    ├── errors.ts                # Error normalization
    └── streaming.ts             # Progress notifications

Design principles

• Thin surface area: Only the endpoints needed for the core capabilities.
• Purpose-driven: Each module serves a specific, well-defined purpose.
• Explicit validation: Zod schemas validate all inputs before API calls.
• Error normalization: All errors converted to {code, message, hint} format.
• Progress streaming: Long-running operations provide visibility.
• Best practices integration: Guidance from Unleash docs embedded in tool descriptions.

Configuration

This section provides a quick reference for all configuration options.

Environment variables:

• UNLEASH_BASE_URL: Your Unleash instance URL (required).
• UNLEASH_PAT: Personal access token (required).
• UNLEASH_DEFAULT_PROJECT: The default project ID the MCP should use (optional).

CLI flags:

• --dry-run: Simulate operations without making actual API calls.
• --log-level: Set logging verbosity (debug, info, warn, error).

Protect production environments

AI assistants can make mistakes and toggle the wrong flag. When change requests are enabled for an environment, the MCP server submits a change request instead of applying the change directly. A human must approve the request before it takes effect, preventing accidental changes whether made manually or by an AI assistant.

Enable change requests on any environment where unreviewed changes pose a risk, especially production. For configuration steps, see change requests.

Best practices

This server encourages Unleash best practices from the official documentation:

Flag lifecycle

1. Create with intent: Choose the right flag type to signal purpose.
2. Document clearly: Write descriptions that explain the “why”.
3. Plan for cleanup: Feature flags are temporary; plan their removal.
4. Monitor usage: Enable impression data for important flags.

Flag types

• Release flags: For gradual feature rollouts (remove after full rollout).
• Experiment flags: For A/B tests (remove after analysis).
• Operational flags: For system behavior (longer-lived, review periodically).
• Kill switches: For emergency controls (maintain until feature is stable).
• Permission flags: For access control (longer-lived, review permissions).

Naming conventions

• Use kebab-case: new-checkout-flow
• Be descriptive: enable-ai-recommendations not flag1.
• Include scope when needed: mobile-push-notifications.

API reference

This server uses the Unleash Admin API. For complete API documentation, see:

• Unleash Admin API OpenAPI Spec
• Unleash API Documentation

Endpoints used

• POST /api/admin/projects/{projectId}/features - Create feature flag

Troubleshooting

Configuration issues

Error: “UNLEASH_BASE_URL must be a valid URL”: Ensure your base URL is complete, including protocol. For example, https://app.unleash-hosted.com/instance. Remove any trailing slashes.

Error: “UNLEASH_PAT is required”: Check that your .env file exists and contains UNLEASH_PAT={{your-personal-access-token}}. Verify that the token is valid in Unleash.

API issues

Error: “HTTP_401”: Your personal access token may be invalid or expired. Generate a new token under Profile > View Profile settings > Personal API tokens > New token.

Error: “HTTP_403”: Your token doesn’t have permission to create flags in this project. Review your role and permissions in Unleash.

Error: “HTTP_404”: The project ID doesn’t exist. Confirm the project ID in Unleash Admin UI.

Error: “HTTP_409”: A flag with this name already exists in the project. Use a different name or reuse the existing flag.

License

MIT

Contributing

This is a purpose-driven project with a focused scope. Contributions should:

• Align with the three core use cases (evaluate and wrap code in feature flags, manage rollouts, clean up after rollout).
• Maintain the thin, purpose-driven architecture.
• Follow Unleash best practices.
• Include clear documentation.