Model Context Protocol (MCP) Server

The Flaggr MCP Server (@flaggr/mcp-server) connects AI coding assistants—such as Claude Desktop, Cursor, VS Code Copilot, and Antigravity—directly to your Flaggr instance.

With the MCP server, AI assistants can:

• List, create, update, evaluate, and toggle feature flags
• Inspect post-toggle safety, health, and error rate drift
• Profile distributed traces and detect server-side latency hotspots
• Measure true SSE real-time stream propagation latency (RTT)
• Inspect granular audit logs with path-scoped diffs
• Execute staged canary rollouts and A/B test experiments

Installation & Setup

1. Claude Desktop

Add @flaggr/mcp-server to your claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "flaggr": {
      "command": "npx",
      "args": ["-y", "@flaggr/mcp-server"],
      "env": {
        "FLAGGR_API_URL": "https://flaggr.dev",
        "FLAGGR_API_TOKEN": "fgr_your_token_here"
      }
    }
  }
}

Tip: If you have already authenticated using the Flaggr CLI (flaggr login), the MCP server will automatically discover your credentials from ~/.flaggr/config.json. You can omit FLAGGR_API_TOKEN.

2. Cursor IDE

Add to your project's .cursor/mcp.json or Global Cursor Settings:

{
  "mcpServers": {
    "flaggr": {
      "command": "npx",
      "args": ["-y", "@flaggr/mcp-server"],
      "env": {
        "FLAGGR_API_URL": "https://flaggr.dev",
        "FLAGGR_API_TOKEN": "fgr_your_token_here"
      }
    }
  }
}

Authentication Methods

The Flaggr MCP server supports three authentication methods:

1. Auto-Discovery via Flaggr CLI: Reads ~/.flaggr/config.json minted by flaggr login. Zero configuration required.
2. Interactive OAuth via login Tool: Prompt the agent: "Log in to Flaggr". The agent calls the login tool, opening your browser to authorize and select a project.
3. Direct Environment Variable: Set FLAGGR_API_TOKEN in your MCP configuration.

Tool Catalog

The MCP server exposes 30+ tools organized into functional domains:

Feature Flag Operations

Post-Toggle Safety & Analytics

Distributed Tracing & Hotspot Detection

Real-Time Streaming & RTT

Audit & Revision History

Progressive Canary Delivery & Rollouts

Targeting Rule Explanation & Blast-Radius Simulator

Adaptive Multi-Armed Bandits & Experimentation

Reusable Customer Cohorts & Dynamic Segments

MCP Resources

Agents can read context directly via dynamic resource URIs:

• flaggr://auth/status — Active token preview, current project, and connection status
• flaggr://projects — List of all accessible projects
• flaggr://project/{slug}/flags — Feature flags in a project
• flaggr://flag/{key}/health — Flag health status
• flaggr://flag/{key}/toggle-impact — Post-toggle safety and drift analysis

MCP Prompts

The server provides pre-packaged prompt templates for complex flag management tasks:

verify_toggle_safety

Prompts the agent to inspect the safety of a recent flag toggle:

Verify post-toggle safety for flag "checkout-v2" in service "web-app".

The agent calls get_flag_toggle_impact, analyzes before/after error rates and P99 latencies, and proposes rolling back if health is degraded.

gradual_rollout

Guides an agent through a staged percentage rollout (5% → 25% → 50% → 100%), verifying health metrics at each step before advancing.

ab_test_setup

Designs an experiment with treatment and control variants, sets traffic allocation, and configures outcome metrics.

Autonomous Agent Workflow Examples

Example: Verifying a Production Flag Toggle

Ask your assistant:

"Check if toggling new-payment-engine in production was safe or if there was any error drift."

The assistant invokes:

{
  "tool": "get_flag_toggle_impact",
  "arguments": {
    "flagKey": "new-payment-engine",
    "serviceId": "checkout-service",
    "environment": "production"
  }
}

The MCP server responds with:

============================================================
FLAG TOGGLE IMPACT REPORT: new-payment-engine
============================================================
Status: [HEALTHY]
Latest Toggle Event:
  - Transition: false -> true
  - Actor:      lead-dev@flaggr.dev
  - Timestamp:  2026-09-12T08:00:00.000Z
  - Propagation Latency: 38ms
 
Before vs After Comparison (±30m window):
┌──────────────────────┬─────────────┬─────────────┬────────────────┐
│ Metric               │ Before      │ After       │ Delta / Ratio  │
├──────────────────────┼─────────────┼─────────────┼────────────────┤
│ Evaluations          │ 2450        │ 2480        │ 1.01x          │
│ Errors               │ 1           │ 1           │ +0             │
│ Error Rate           │ 0.04%       │ 0.04%       │ +0.00%         │
│ Avg Latency          │ 1.35ms      │ 1.34ms      │ -              │
│ P99 Latency          │ 3.80ms      │ 3.75ms      │ -0.05ms        │
└──────────────────────┴─────────────┴─────────────┴────────────────┘
 
Automated Recommendation:
Healthy: Post-toggle metrics are stable with no significant error or latency regression.
============================================================

The assistant summarizes the findings and confirms the deployment is stable.

Next Steps

• REST API Reference — Core Flaggr REST endpoints
• Flag Health Guide — Post-toggle safety and drift diagnostics
• OpenFeature SDKs — Evaluate flags in your application code