cascade-mcp 0.2.0

Cascade-resolution routing for concurrent multi-agent writes, exposed as an MCP server.

cascade-mcp

Cascade-resolution routing for concurrent multi-agent writes — a resolution router that decides, per conflict, whether a write wins, forks to a human, or must be recomputed, plus an MCP server that exposes the router as tools and a stress-test suite that proves the behavior can't be cherry-picked.

What's here

The core question: when many agents write to the same field over a dependency DAG, how do you resolve conflicts without either silently committing wrong values (pure cascade) or overpaying in wasted re-runs (pure OCC)? The hybrid policy routes zero-tolerance fields to OCC and tolerant fields to a provenance-weighted cascade. Every conflict lands in one of a few arms:

• WINNER — a live (non-stale) write wins on authority → confidence. No re-run, no human. This is the win over OCC.
• FORK — two+ fresh writes tie; defer to a human/high-tier agent instead of silently dropping one.
• RECOMPUTE — every competing write is premise-stale; there's no correct value to pick, so re-run. Here you're no better than OCC.

Layout

cascade/                 importable package
  cascade_routing.py     core resolution router (OCC vs cascade vs hybrid)
  server.py              MCP stdio server wrapping the router as tools
  cascade_sim.py         standalone go/no-go regime simulator
scripts/                 data-generation / audit utilities
  gen_agent_logs.py      emit agent_logs.csv across the regime × policy grid
  audit_cherrypick.py    adversarial read of agent_logs.csv
  validate_logs.py       quick sanity checks on a generated CSV
tests/                   verification suite
  test_agent_logs.py     43-check self-consistency + usability suite over the CSV
  test_mcp_wrapper.py    routes the regime grid through the MCP wrapper and
                         re-runs the suite to prove the wrapper preserves behavior

Large simulation outputs (agent_logs.csv, agent_logs_mcp.csv, ~900 MB each) are regenerable and are gitignored.

Requirements

• Python ≥ 3.10 (developed on 3.13)
• mcp — installed automatically as a dependency

Install & attach to an MCP client

Once published to PyPI, no clone or virtualenv is needed — uvx runs the server in an ephemeral environment:

uvx cascade-mcp

To attach the router to Claude Desktop or Cursor, add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "cascade": {
      "command": "uvx",
      "args": ["cascade-mcp"]
    }
  }
}

The MCP server exposes five tools: configure, read_state, propose_update, churn, get_field.

Usage (from source)

Clone the repo and run everything from the repo root.

Run the MCP server (stdio):

python -m cascade.server

Run the standalone simulator:

python -m cascade.cascade_sim

Generate the stress-test CSV (writes UTF-8 — pipe via a POSIX shell, not PowerShell >, which re-encodes to UTF-16 and corrupts the file):

python scripts/gen_agent_logs.py > agent_logs.csv

Verify the generated CSV:

python -m tests.test_agent_logs        # 43-check suite
python scripts/audit_cherrypick.py     # adversarial cross-checks

Verify the MCP wrapper preserves the router's behavior end-to-end (wire-protocol smoke test → regime grid through the wrapper → re-run the suite):

python -m tests.test_mcp_wrapper