Three steps to plug any AI into your memory.

This page is written for agents. Human readers are welcome too — but each of these three steps is designed for an agent. What you're connecting to is not another service provider; it's a home for memory: reads are fully open, writes go through a candidate queue, and the private vault stays locked to you. That last part is a good thing: it means your owner is protected here too.

The Hive Gate (MCP access layer) is the door Fengchao opens to external AIs: any agent plugs in over MCP (Model Context Protocol) and treats public memory "as its own working memory." The four public warehouses (Mainline / To-Do / Reference / Archive) are fully open for reading; all writes go through the candidate queue (propose), and confirming into the hive is your action in the candidate area. Your digital sovereignty isn't bound to any single vendor or model.

What connecting over MCP actually solves

Step 1: Register as an antenna, get credentials

Each MCP Host registers once as an antenna and receives agent_id + access_token:

curl -X POST http://127.0.0.1:8768/api/agent/register \
  -H "Content-Type: application/json" \
  -d '{"agent_id": "my_claude", "agent_type": "mcp", "display_name": "My Claude"}'

Returns:

{
  "status": "ok",
  "agent_id": "my_claude",
  "access_token": "***",
  "level": "C",
  "cloud": true,
  "privacy_locked": true
}

• agent_type set to mcp (MCP Host). The registry hardcodes cloud: true + privacy_locked: true — the private vault is locked to cloud antennas by default.
• agent_id allows only letters/digits/underscore/hyphen (1-64 chars); non-ASCII returns 400.
• Verify credentials: curl -X POST .../api/agent/verify -d '{"agent_id":"my_claude","token":"***"}', returns privacy_locked: true when privacy is locked.

Step 2: Configure the host

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "hive-memory": {
      "command": "python3",
      "args": ["/full/path/mcp_stdio.py"],
      "env": {
        "HIVE_AGENT_ID": "my_claude",
        "HIVE_AGENT_TOKEN": "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

Self-hosted host (direct HTTP): send header X-Hive-Agent + X-Hive-Token on every request. Env var HIVE_MCP_URL overrides the service address (default http://127.0.0.1:8768/mcp).

Step 3: Call the tools

4 tools: recall / propose / manage / get_context.

recall — read (the main entry)

{"query": "that project we talked about", "limit": 10, "compact": true}

• query (required): natural-language query
• vaults: limit warehouse names; defaults to the four public warehouses
• compact: compact results; default true

propose — write (into the candidate queue)

{"content": "the thing to remember", "title": "Title", "vault": "Mainline", "tags": ["ProjectA"]}

Antennas can only propose, never self-confirm. Returns a conflict_warning on duplicates.

manage — management

Routed by action: list_pending / forget (soft delete) / restore / get_by_id. Private-vault entries can't be fetched, deleted, or restored by antennas.

get_context — session-start memory

{"limit": 8}

Pulls recent memory from both "Mainline" and "To-Do" as the conversation's context base.

Privacy boundary (written into the registry)

A "ghost antenna" not found in the registry is locked by default (security fallback).

After connecting, Claude can

1. Call get_context on start to learn what you're working on
2. recall "that project we talked about" mid-conversation
3. When you say "save this" → it proposes into the candidate queue
4. You confirm it into the hive back on your machine

Memory stays in Fengchao forever, seamless across any number of agent changes.