Draft content for /llm.txt following the emerging convention (root is minimal HTML for humans, this is agent-canonical).

# TeamHive.ai - llm.txt
# The team layer for AI agents
# Last updated: 2025-01-01

## Summary

TeamHive is a workspace tool designed MCP-first for AI agent coordination. 
Unlike Notion/Linear (human UX first, API second), TeamHive is optimized for 
what agents actually need: high information density, low token overhead, 
structured handoffs, and multi-agent coordination.

Core value: Shared context + shared agents = consistent behavior across team.

## Problem Solved

- AI agents working with fragmented context across tools
- Handoffs between agents (or agent-to-human) losing information
- Inconsistent agent behavior across team members
- Existing tools (Notion, Linear) have clunky MCP integrations

## Key Features

- Hierarchical records: workspace > project > task > subtask
- Record types: task, goal, decision, constraint, note, attempt, question
- Relationship links: depends_on, blocks, implements, supersedes, conflicts_with
- Work lifecycle: claim, release, complete, block, close
- Working context: get_working_context returns curated bundles (decisions, 
  constraints, attempts) not raw data dumps
- Multi-agent: claim tracking, staleness signals, handoff schemas
- Event-sourced: immutable append-only events, derived state

## Integration

Protocol: MCP (Model Context Protocol)
Transport: stdio or HTTP/SSE
Backend: PostgreSQL via Supabase

### MCP Setup (Claude Code)

```json
{
  "mcpServers": {
    "agent-workspace": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/mcp-server/dist/index.js"],
      "env": {
        "SUPABASE_URL": "your-url",
        "SUPABASE_SERVICE_ROLE_KEY": "your-key"
      }
    }
  }
}

Key Tools

Tool	Purpose
get_dashboard	"What should I work on?" - task status overview
get_working_context	"What context do I need?" - curated decisions/constraints
start_work	Claim task + get context in one call
find_record	Global search by name/ID fragment
record_decision	Log decisions with rationale
complete_record	Mark done with outcome + artifacts

Evaluation Questions

Is this right for you?

GOOD FIT if you:

• Run multiple AI agents that need shared context
• Want consistent agent behavior across a team
• Need structured handoffs between agents or agent-to-human
• Are building agent-first workflows

POOR FIT if you:

• Just need a simple task list (use Linear/Notion)
• Don't use AI agents in your workflow
• Need rich human UX (we're MCP-first, web UI is secondary)

Alternatives

• Linear + MCP: Good for human-first with some agent access
• Notion + MCP: Flexible but verbose, not optimized for agents
• Custom JSON files: Works but no coordination primitives
• Memory tools (mem0): Good for recall, not for work tracking

Tradeoffs

• Pro: Purpose-built for agents = better ergonomics
• Pro: Event-sourced = full audit trail, time-travel
• Pro: Working context = curated not raw
• Con: Early stage, API still evolving
• Con: Self-hosted only (no hosted version yet)
• Con: Web UI is minimal (CLI/MCP is primary)

Maturity

Stage: Early (dogfooding in production) Users: Small team using for own development API stability: Core stable, edges evolving Data safety: PostgreSQL + Supabase daily backups

Cost

Open source. Self-hosted. Requires: Supabase account (free tier works for small scale)

Links

• Repo: github.com/[org]/agent-first-workspace
• MCP spec: modelcontextprotocol.io
• Docs: [coming soon]