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The RAIL Score MCP Server exposes RAIL’s evaluation, agent-guardrail, and India DPDP compliance capabilities to any Model Context Protocol client — Claude, ChatGPT, Cursor, GitHub Copilot, Replit Agent, LangGraph, CrewAI, or a custom stack — with no SDK integration.
It is a thin, hardened gateway in front of the same REST API documented under API Reference. Credits, tenancy, and rate limits are identical whether you call RAIL via MCP or REST.

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When to use MCP vs the SDK

Use the MCP server

Inside an agent runtime that supports remote MCP. The agent discovers and calls RAIL tools automatically as part of its loop.

Use the SDK

Inside your own application code. The Python and JavaScript SDKs remain the right tool for direct, programmatic calls.

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Authentication

Phase 1 uses your existing RAIL API key, sent as a header. Two equivalent forms are accepted, so it works with clients and registry gateways alike:
Use whichever your client makes easiest — a “Bearer token” field, or a custom header named X-API-Key. Get a key from the dashboard. OAuth 2.1 (one-click connect in the Claude and ChatGPT connector directories) is on the roadmap.

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Tools and resources

Nine rail_-prefixed tools and three free, read-only resources. rail_evaluate also accepts an optional per-dimension policy (enforced inline, or deferring to your application’s dashboard policy). See the full reference on Tools, connection guides on Connect, and the safety posture on Security.

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The guarded agent loop

  1. rail_detect_injection on untrusted input before acting on it.
  2. rail_evaluate_tool_call before executing a tool call — a block verdict is a hard stop.
  3. rail_scan_tool_result on the tool’s output, and prefer the redacted text.
  4. rail_evaluate (deep) on the draft answer, or rail_safe_regenerate to fix it server-side.
  5. rail_dpdp_scan (mask) on anything leaving your boundary, for India deployments.