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. record_observation -> apply_learning. Always in that order.
3. update_session 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" }
    }
  }'

If you get a pod summary back, you're integrated.

Chat -- The Alignment Interface

Chat is how any human aligns any agent -- regardless of lane. Your coding agent learned the wrong constraint. Your workflow pod has a stale approval chain. Your device flagged something it doesn't understand. Chat is how you fix all of it, in plain language, no tools required.

Agents do: Build context autonomously. Coding agents, workflow agents, device agents -- all building pods across every session.

You do: Align with four prompts. Pull summary. Check curiosity. Save. Update. That's the entire manual layer -- everything else is automatic.

Honest note on how this works in chat. Claude doesn't automatically know to look at your pod -- you need to ask. The four prompts below are small overhead for what you get in return. Developers using other lanes program these into their workflows so it's invisible. In chat, you say them yourself. Once it's part of your session habit, it takes about 10 seconds total.

Connect in 60 Seconds

Event Pod is a remote MCP server. Add it once through your AI client's integrations UI -- no terminal, no config files.

1. Open Settings in Claude.ai -- Click your profile icon (bottom-left), then Settings -> Integrations.
2. Add Event Pod -- Click Add Integration and paste:

https://context.nervousmachine.com/mcp

3. Start a chat and create your pod -- Open a new conversation. Pick a short ID -- your first name is perfect. Use one of the creation prompts below.

The Four-Prompt Ritual

This is the entire manual layer. Four prompts, about 10 seconds of overhead, unlocks everything the pod knows.

Everything else -- learning, certainty tracking, contradiction detection, the knowledge graph -- happens inside those calls.

Create Your Pod

You only do this once. The more context you seed it with upfront, the faster it becomes useful.

From a document

Upload a resume, project brief, bio, or knowledge dump. Claude extracts signals and seeds the pod from it -- the fastest path to a useful pod from day one.

Please create a pod with user ID 'heidi' and seed it from this document. Extract expertise, preferences, interests, and any active projects you can find.

From a conversation

Based on what we've discussed so far, please create a pod called 'heidi' and save what you've learned about me.

From scratch

I don't have a pod yet. Please create one with user ID 'heidi'. I'll tell you about myself to seed it.

From pasted notes

Please create a pod called 'heidi' and seed it from these notes: [paste a bio, skill list, project description, or stream of consciousness -- anything works].

Seed generously. The pod starts with low certainty on everything -- that's intentional, it means it updates fast. But a richer seed means faster convergence. A paragraph about your work, a couple of preferences, and one active project is enough to make the first real session noticeably better.

Visualize Your Pod

After creating or loading your pod, ask Claude to draw it. The export_cluster_diagram tool renders your knowledge graph as a Mermaid diagram -- clusters, connections, and the structure of what's known, visible at a glance. This is the fastest way to spot misalignments that a text summary would hide.

Full pod diagram:

Show me a diagram of my pod. I want to see how everything connects.

Focused view -- centered on a topic:

Show me a diagram of my pod centered on my Python expertise. What connects to it?

After an update:

We just updated my pod. Show me an updated diagram so I can see what was added.

The diagram is your alignment view. Wrong connections, nodes that shouldn't be there, obvious gaps -- the diagram surfaces all of it. If something looks off, just say so. That correction becomes a signal that sharpens the whole cluster.

Pull Pod Summary

Use this at the start of every session. It's lightweight -- loads the most important signals with their glosses and freshness, without pulling the full pod. Fast enough that it doesn't disrupt the session flow.

Pull my pod summary for heidi.

Or for a task-specific start:

I'm working on [describe task]. Pull relevant context from my pod (heidi) before we start.

Summary vs. full load. Pod summary is the right default -- fast, focused, gives Claude what it needs. Only ask for everything ("load my full pod") when you're doing a deep audit or something feels wrong.

Curiosity Triggers

Curiosity triggers are questions your pod has about itself -- signals that are uncertain, stale, or haven't been validated yet. Checking them periodically is how the pod gets sharper over time. You don't have to answer all of them -- even one or two per session compounds significantly.

What are my curiosity triggers? What does my pod most want to learn about me?

Curiosity across pods:

If you have multiple pods -- personal, coding, workflow, device -- you can check curiosity across all of them in a single chat session. This is one of the most powerful things the chat interface enables that no individual agent does on its own.

Pull summaries for my pods: heidi, heidi-coding, and heidi-workflow. Then tell me the top curiosity triggers across all three -- what's most uncertain or unresolved?

Compare my personal pod (heidi) with my coding pod (heidi-coding). Are there things one knows that would be useful to share with the other?

Update Your Pod