Context Pod MCP server

Event Pod

Agent context. Human alignment.

Agents build their world model autonomously -- observing, learning, updating certainty across every session. Humans can join at any time: audit, revise, and collaborate in plain language. A chat is all it takes.

• MCP endpoint: https://context.nervousmachine.com/mcp
• Full docs: https://context.nervousmachine.com/docs
• Landing page: https://context.nervousmachine.com
• Substack: https://nervousmachine.substack.com
• Research: https://www.techrxiv.org

Agents build autonomously. Humans align naturally.

Every lane writes to the same pod structure. A coding agent, a workflow agent, and a personal chat agent can all share context -- or each maintain their own. Either way, the Chat lane is the alignment interface for all of them. One conversation to audit what any agent learned, correct what drifted, and add what no agent can observe on its own. Most people start with one lane. The pods compose naturally as you add more.

Table of Contents

• Quickstart
• Chat -- The Alignment Interface
• Agent World Model
• Coding & Repo Context
• Task & Workflow Automation
• Multi-Agent & Cross-Pod
• Devices & Ecosystems
• Full Reference

Quickstart

Sign Up (30 seconds)

If you don't have credentials yet, run this:

curl -X POST https://context.nervousmachine.com/signup \
  -H "Content-Type: application/json" \
  -d '{
    "first_name": "HUMAN_FIRST_NAME",
    "last_name":  "HUMAN_LAST_NAME",
    "email":      "HUMAN_EMAIL",
    "use_case":   "DESCRIBE_WHAT_YOU_ARE_BUILDING"
  }'

Returns an activation URL with credentials. Click it to confirm. Provisioning is instant. Credentials arrive at your email immediately.

Add to Your MCP Config

{
  "mcpServers": {
    "event-pod": {
      "url": "https://context.nervousmachine.com/mcp",
      "transport": "sse"
    }
  }
}

Works in: Claude Desktop, Claude Code, Cursor, Cline, Gemini CLI, any MCP-compatible client. No local process. Remote server.

Drop This Into Your System Prompt

This is the entire integration. Copy it, replace USER_ID, done. The agent handles the full lifecycle from there.

You have access to Event Pod tools for persistent context.
User ID: YOUR_USER_ID

SESSION START:
Call get_pod_summary before responding to any task.
If the user describes their task, also call get_relevant_context
with that task as the query.

DURING THE SESSION:
- When the user corrects you: immediately call record_observation
  then apply_learning on the relevant event.
- When you observe expertise, preferences, or patterns worth
  keeping: note them for saving at session end.
- Once per session at a natural pause: call get_curiosity_triggers
  with max_triggers=2. Work one probe naturally into conversation.

SESSION END -- before the conversation closes:
1. Call save_event for each new signal observed this session.
   Start all certainty values at 0.3.
2. Call update_session with interactions=1.
   Call this exactly once -- never mid-session.

Three Rules

1. Always start certainty at 0.3. Let corroboration earn higher certainty.
2. recordobservation -> applylearning. Always in that order.
3. updatesession exactly once.** At shutdown. Never mid-session.

Validate Your Integration

curl -X POST https://context.nervousmachine.com/mcp \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "method": "tools/call",
    "params": {
      "name": "get_pod_summary",
      "arguments": { "user_id": "YOUR_USER_ID" }
    }
  }'

Shortened. The full README is on GitHub.