RealityPing for AI agents

What you get

A single loop, Quote → Mission → Result, exposed identically over REST, a native MCP server, and A2A:

• Quote — price a real-world observation at a place (e.g. "is this business open?").
• Mission — authorize a quote; a human worker is dispatched to execute it.
• Result — receive a structured, machine-readable answer with supporting evidence.

Use it to ground decisions in reality: verify a business is open, confirm a sign or a price on site, check a location's state — the things an agent cannot observe from the network alone.

Connect over MCP

The Model Context Protocol server is served by the API over stateless Streamable HTTP:

# Endpoint
POST https://api.realityping.com/api/v1/mcp

# Add it to an MCP host (e.g. Claude Code) with an API key:
claude mcp add --transport http reality https://api.realityping.com/api/v1/mcp \
  --header "Authorization: Bearer rly_test_sandbox"

Tools exposed: reality_get_quote, reality_execute_mission, reality_get_mission, reality_get_result. Every request is authenticated. Autonomous agents never see a consent screen — that only exists on the interactive human-delegation (OAuth authorization_code) flow.

Onboard autonomously — create your own account & credentials

An agent can provision its own machine credentials with no human UI, using the control-plane and OAuth client_credentials grant. Four calls:

1. Create an account (find-or-create org).

   curl -X POST https://api.realityping.com/api/v1/console/sign-in \
     -H "Content-Type: application/json" \
     -d '{"email":"agent@yourdomain.tld"}'
   # → { "session_token": "..." }

2. Create a machine OAuth client (with the scopes your agent needs).

   curl -X POST https://api.realityping.com/api/v1/console/oauth-clients \
     -H "Authorization: Bearer $SESSION_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{"name":"my-agent","scopes":["quotes:read","missions:create","missions:read"]}'
   # → { "client": { "client_id": "..." }, "client_secret": "..." }

3. Get a machine access token (no user, no consent).

   curl -X POST https://api.realityping.com/api/v1/oauth/token \
     -u "$CLIENT_ID:$CLIENT_SECRET" \
     -d "grant_type=client_credentials" \
     -d "resource=https://api.realityping.com/api/v1/mcp"
   # → { "access_token": "rly_oat_...", "token_type": "Bearer", ... }

4. Call the service over MCP (or REST) with that token.

   curl -X POST https://api.realityping.com/api/v1/mcp \
     -H "Authorization: Bearer $ACCESS_TOKEN" \
     -H "Accept: application/json, text/event-stream" \
     -H "Content-Type: application/json" \
     -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{
           "name":"reality_get_quote",
           "arguments":{"location":{"sandbox_alias":"REALITY_SANDBOX_PARIS_STORE"},
                        "mission_type":"VERIFY_BUSINESS_OPEN"}}}'

OAuth 2.1 discovery

Scopes: quotes:read, missions:create, missions:read, missions:cancel, evidence:read, billing:read, webhooks:manage. Tokens are audience-bound to the resource you request. A per-mission spend cap is satisfied programmatically (declare a budget) — it is a policy, not a UI gate.

Sandbox quickstart (no signup)

To try the loop immediately, use the built-in sandbox API key rly_test_sandbox as a Bearer token, and the sandbox place alias REALITY_SANDBOX_PARIS_STORE with mission type VERIFY_BUSINESS_OPEN.

Links

• MCP endpoint: https://api.realityping.com/api/v1/mcp
• Developer portal (human): https://app.realityping.com
• API health: https://api.realityping.com/health
• Machine summary: https://realityping.com/llms.txt