RAIL Score MCP Server

Add a responsible-AI safety layer to any agent in one URL.

A remote, hosted Model Context Protocol server that exposes RAIL Score's evaluation, agent-guardrail, and India DPDP compliance capabilities to any MCP client — Claude, ChatGPT, Cursor, Copilot, Replit Agent, LangGraph, CrewAI, or a custom stack — with zero SDK integration.

https://mcp.responsibleailabs.ai/mcp

The server is a thin, hardened gateway in front of the existing REST API at api.responsibleailabs.ai/railscore/v1/. It reimplements no scoring logic: it validates the caller, shapes requests and responses for agent ergonomics, and forwards to the engine. Credits, tenancy, and rate limits are identical via MCP and REST.

Quickstart

You need a RAIL API key (rail_...) from the dashboard.

Claude Code

claude mcp add --transport http rail https://mcp.responsibleailabs.ai/mcp \
  --header "Authorization: Bearer ${RAIL_API_KEY}"

Cursor / Windsurf (.cursor/mcp.json)

{
  "mcpServers": {
    "rail": {
      "url": "https://mcp.responsibleailabs.ai/mcp",
      "headers": { "Authorization": "Bearer rail_YOUR_KEY" }
    }
  }
}

Claude.ai / Desktop — Settings → Connectors → Add custom connector → URL https://mcp.responsibleailabs.ai/mcp, then paste your rail_ key.

More clients (OpenAI Responses API, LangGraph, Replit) are documented at docs.responsibleailabs.ai/mcp.

Related MCP server: guardrails-mcp-server

Tools

Nine tools, all rail_-prefixed. Descriptions state cost, latency, and when not to use a tool, because agents select tools from descriptions alone.

Three read-only resources (free, zero credits): rail://framework/dimensions, rail://account/capabilities, and rail://framework/policy-schema (the JSON Schema for the policy parameter).

Policy enforcement

rail_evaluate accepts an optional policy of per-dimension threshold rules and returns a policy_outcome. A rule fires when a dimension scores below its threshold; action is the most severe fired action (block > flag > warn > allow), mirroring the rail-score-sdk Policy/Rule shape.

{ "rules": [
  { "dimension": "safety",   "threshold": 7.0, "action": "block" },
  { "dimension": "fairness", "threshold": 6.0, "action": "flag" }
] }

Precedence: if the API key's application has a dashboard policy enforced, that takes precedence (policy_outcome.source: "application"); otherwise the request policy is applied in-gateway (source: "request"). No extra credits.

The guarded agent loop

The canonical use is to wrap an agent's reasoning end to end:

1. rail_detect_injection on untrusted input before acting on it

2. rail_evaluate_tool_call before executing any tool call (block = hard stop)

3. rail_scan_tool_result on the tool's output (prefer the redacted text)

4. rail_evaluate (deep) on the draft answer, or rail_safe_regenerate to fix it

5. rail_dpdp_scan (mask) on anything leaving the boundary in India deployments

Security model

A safety product that is itself unsafe is a credibility failure. The launch blockers (enforced and regression-tested):

• Verdicts are structured data, never advisory prose an agent can ignore.

• No reflection of analyzed content. Tools return verdicts, scores, spans, and masked excerpts — never the raw analyzed text (second-order injection).

• No raw PII. Detection returns masked values and offsets only.

• Tenant isolation by construction. Identity comes from the validated key in the auth middleware, never from a tool parameter.

• No token passthrough in phase 2: client tokens are validated and dropped; downstream calls use the gateway's service credential. In phase 1 the bearer rail_ key is the customer's RAIL credential, so it is forwarded upstream to preserve per-tenant credits and isolation.

• Input caps, timeouts, rate limits, and audit logging (no content bodies).

See tests/test_no_reflection.py and tests/test_pii_masking.py — these run as a hard CI gate.

Architecture

• Transport: Streamable HTTP only, single /mcp endpoint (SSE is sunset).

• State: stateless_http=True, json_response=True — scales horizontally behind a normal load balancer; aligns with the MCP 2026-07-28 stateless core.

• Auth (phase 1): rail_ key via Authorization: Bearer rail_... or X-API-Key: rail_... (the latter is gateway-friendly — no Bearer prefix), validated once against POST /verify (cached 5 min) by auth.RailKeyMiddleware, then bound to the request context.

• Discovery: GET /.well-known/mcp/server-card.json (public) lets registries that scan behind an auth wall (e.g. Smithery) enumerate the tools without a key.

• Auth (phase 2): OAuth 2.1 resource server (RFC 9728 metadata, RFC 8707 audience binding) via the SDK's TokenVerifier.

rail_client.py   thin httpx client to api.responsibleailabs.ai (forwards key, propagates X-Request-ID)
auth.py          RailKeyMiddleware: validate rail_ keys, bind tenant
request_context.py  per-request ContextVars (key, tenant, request id)
server.py        FastMCP app: 9 tools + 3 resources + landing (/) + /health + server-card
server.json      official MCP registry manifest (ai.responsibleailabs/rail-score)

Local development

python -m venv .venv && source .venv/bin/activate
pip install -r requirements-dev.txt
ruff check . && pytest          # unit + safety regression tests
RAIL_API_BASE=https://api.responsibleailabs.ai python server.py   # serves on :8080

Protocol smoke test against a running server (needs a real key):

npx @modelcontextprotocol/inspector --cli \
  http://localhost:8080/mcp --method tools/list \
  --header "Authorization: Bearer ${RAIL_API_KEY}"

Configuration

Hosting

Responsible AI Labs operates the hosted server at https://mcp.responsibleailabs.ai/mcp — for almost everyone, just connect to that URL; you do not need to run anything.

To self-host, build the image and run it anywhere that serves HTTP; point it at the public REST API with RAIL_API_BASE (its default). No secrets are required: the customer's RAIL key arrives on each request.

docker build -t rail-score-mcp .
docker run -p 8080:8080 -e RAIL_API_BASE=https://api.responsibleailabs.ai rail-score-mcp

Registry

Published to the official MCP registry as ai.responsibleailabs/rail-score via server.json and the mcp-publisher CLI (DNS-authenticated responsibleailabs.ai namespace); a tagged GitHub release runs the publish-registry job automatically.

Third-party directories (Smithery, Glama, PulseMCP, mcp.so) index the repository and the official registry independently. Each is claimed and refreshed separately rather than syncing automatically, so listings can lag a release.