Use it from an agent

Any agent harness that can run shell commands can operate Ringee through the CLI — no MCP client implementation required. Install it, export the connection, and have the agent call commands with --json.

npm i -g ringee
export RINGEE_MCP_URL="https://api.ringee.io/api/mcp/<userId>/sse"

ringee config check                 # confirm connectivity
ringee contacts search acme --json  # machine-readable for the agent

This works with Claude Code, OpenClaw, Hermes, a cron job, or your own loop.

If your harness speaks MCP natively, connect the MCP server directly instead — you get typed tool schemas and the client can surface confirmation prompts itself.

Why this is safe

The guardrails live in the CLI, not in the prompt. An autonomous agent cannot spend credits, mint a magic link or delete a contact without passing a flag that encodes a deliberate decision:

Action	Required
`leads reveal`	`--yes`
`sessions create`	`--yes`
`sessions revoke`	`--yes`
`contacts delete`	`--confirm-phone <storedPhone>` and `--yes`

Without them the command exits non-zero with an explanation, which the agent can relay to the user instead of guessing.

Brief the agent

Three commands print the shared operating knowledge, no connection needed:

ringee tools    # capability catalog with sensitivity tags
ringee flow     # the outbound flow, step by step
ringee prompt   # the full agent system prompt

Feeding ringee prompt into your agent’s system message gives it the same rules the Claude skills and the ChatGPT app follow — which tool maps to which action, the prospect → contact → session → outcome → follow-up flow, and when to stop and ask.

A typical loop

Resolve before acting

ringee contacts search "ada" --json

Never act on an id the user did not approve.

Do the work

ringee outcomes log <callId> interested --note "Wants pricing" --json
ringee callbacks create <contactId> 2026-05-24T10:00:00-04:00 --json

Stop at sensitive steps

Ask the user, then pass the flag:

ringee sessions create --contact <id> --title "Tue outbound" --yes --json

Check exit codes

Sensitive commands exit non-zero when the confirmation flag is missing. Treat that as “ask the human”, not as a bug to work around.

Build on the agent layer directly

If you are writing TypeScript instead of shelling out, use @ringee-io/agent — the package the CLI itself is built on. It contains no business logic: it validates input, talks to the MCP, and shares the catalog, flows, prompts and rules.

import { RingeeClient } from "@ringee-io/agent";

// Reads RINGEE_MCP_URL, or RINGEE_BACKEND_URL + RINGEE_USER_ID [+ RINGEE_ORG_ID]
const ringee = RingeeClient.fromEnv();

const { contacts } = await ringee.searchContacts({ query: "acme" });

const created = await ringee.createContact({
  phoneNumber: "+14155552671",
  name: "Jane Doe",
});

// Sensitive — confirm with the user first:
const session = await ringee.createCallSession({
  title: "Tuesday outbound",
  contacts: [{ contactId: created.contact!.id }],
});
console.log(session.joinUrl); // share exactly as returned

await ringee.close();

Reuse the operating knowledge in your own prompts:

import { buildSystemPrompt, TOOL_CATALOG, PRIMARY_FLOW } from "@ringee-io/agent";

Export	Purpose
`RingeeClient`	Typed facade — one method per capability
`RingeeMcpClient`	Lower-level MCP SSE transport wrapper
`TOOL_CATALOG`	Action → tool map with sensitivity classes
`PRIMARY_FLOW`	The outbound flow definition
`buildSystemPrompt()`	Composed prompt from catalog, flow and rules
`schemas`, `types`	Zod input schemas and result shapes

Next steps

Claude skills and apps

Ready-made /ringee commands for Claude and ChatGPT

Safety model

What the server enforces regardless of the client

Get Started

Reference

Why this is safe

Brief the agent

A typical loop

Build on the agent layer directly

Next steps

Claude skills and apps

Safety model

​Why this is safe

​Brief the agent

​A typical loop

​Build on the agent layer directly

​Next steps

Claude skills and apps

Safety model

Why this is safe

Brief the agent

A typical loop

Build on the agent layer directly

Next steps