MCP connector · live now

Connect Thread Otter to your AI.

Add Thread Otter to Claude, ChatGPT, or Claude Code as an MCP connector. Then ask your AI about your GTM: it reads your project and proposes the work, in your voice. The judge, the pacing, and the ban guards stay ours, so nothing it proposes can burn your accounts.

Start free trial See the teammate

the product works without any of this · the connector is for founders who already work inside an AI assistant

three ways to chat with Otter

In the app

The assistant sits beside your queue. Ask what to post, who is worth a reply, or for a draft on the spot.

live · every plan

In Slack

Add Thread Otter to your workspace and mention it in your project channel. The answer lands in the thread.

live · Settings › Slack

From your own AI

Connect Claude, ChatGPT, or Claude Code over MCP. Same tools, same guardrails: it proposes, it never sends.

live · this page

SOCKETone connector, any agent

One MCP server. Every agent speaks it.

A remote MCP endpoint with a project-scoped key. Add it to Claude or ChatGPT as a connector, to Claude Code as a server, or call it from any MCP client you run. No SDK, no webhook maze, no OAuth dance.

Claude + ChatGPT connect as a custom connector

Claude Code + agent frameworks connect as an MCP server

Keys are scoped to one project, revocable any time

Add to your AI

{
  "mcpServers": {
    "thread-otter": {
      "type": "http",
      "url": "https://www.threadotter.com/api/mcp",
      "headers": {
        "Authorization": "Bearer tomcp_..."
      }
    }
  }
}

live · create a key under Settings › MCP, paste it here

HANDSwhat your AI can do

Read everything. Propose anything.

The same tools our own assistant works with, exposed to yours. Reads are instant. Writes are proposals: they land in the pipeline as drafts, never on a platform.

Reads

list_top_signals

scored buyer mentions across every watched source

read_conversation_thread

a full conversation with its draft + outcome history

list_people

the person-centric directory: signals + cold imports

read_post_performance

sends, replies, and outcomes per channel

read_product_brief

the product brief + docs the drafts are grounded in

Proposals

engage_signal

draft a reply to a signal, in the founder's voice

create_posts

brief posts as suggestions: the planner fans them out per channel

set_draft_content

submit your AI's own text into the pipeline

a stable, versioned tool set · reads are instant, writes are proposals

RAILSwhy it can't hurt you

Your AI gets the hands, never the send button.

There is no raw send endpoint, by design. A badly prompted agent can waste its own rate limit; it cannot spam Reddit from your account. Every proposal goes through the exact pipeline autopilot uses.

That is the difference between an API that posts and one you would trust to touch your founder account.

Every write, same rails

Your AI proposesa reply, a post brief, or its own draft text

The judge reads itsame gate as autopilot: bot-tells and self-promo stripped, weak drafts rejected

Pacing schedules itper-account caps and human cadence, per channel

Then it sendsthrough your session or a managed account, with the receipt on the thread

judge · pacing · ban guards · receipts, on every proposal

DOORSways in, in order

MCP is live. The rest follows.

Remote MCP server

the connector above: reads + proposals for any MCP client

live now

Agent skill

install into Claude Code: our playbook baked in, so drafts follow strategy, not vibes

planned

CLI

scripts + cron: pull analytics, queue briefs from your pipeline

planned

REST API

the same surface as plain HTTPS for everything else

planned

included with your plan · no separate API tier

Your AI, our rails.

MCP is live today. Tell us what to build next, the CLI, an agent skill, or a REST API, and we will build against real workflows, not guesses.

Share what to build next See what the teammate does

MCP is live today · the CLI, agent skill, and REST doors are next