MCP Server¶

Included directly from dense_armor/mcp_server/README.md so it never goes stale relative to the single source of truth.

dense_armor_mcp¶

An MCP server that lets Claude (or any MCP client) call Dense-Armor's anomaly-shielding functions directly -- clean a corrupted series, classify each point as an isolated spike vs a genuine regime change, or run the classic robust-statistics detectors -- without writing Python.

Unlike Dense-Evolution's MCP adapter (a thin HTTP proxy to a separately running FastAPI kernel behind its Composer web UI), this one is direct, in-process: Dense-Armor has no web UI/kernel to share, and its operations are lightweight pure NumPy/JAX calls with no reason to live in a separate long-running process. Every tool here imports and calls dense_armor directly, in this same process -- no separate server to start first.

1. Install this server's dependencies¶

pip install -e ".[mcp]"           # if you have the repo checked out
pip install "dense-armor[mcp]"    # from PyPI, once published
# or, standalone without the extras mechanism:
pip install -r dense_armor/mcp_server/requirements.txt

2. Register it with your MCP client¶

Claude Code:

claude mcp add dense_armor -- dense-armor-mcp

dense-armor-mcp is the console-script entry point ([project.scripts] in pyproject.toml, separate from dense-armor itself, Armatura's own CLI) -- it just calls this file's main(). Running python /absolute/path/to/dense_armor/mcp_server/server.py directly still works too, e.g. if you haven't installed the package.

Note: this lives at dense_armor.mcp_server, not a bare mcp_server -- Dense-Evolution's own adapter is (confusingly) also just called mcp_server, and with both packages installed in the same environment a bare top-level name is a real, observed collision, not a hypothetical one.

Manual .mcp.json / claude_desktop_config.json entry:

{
  "mcpServers": {
    "dense_armor": {
      "command": "dense-armor-mcp"
    }
  }
}

Tools¶

Tool	What it does
`dense_armor_health`	Confirms `dense_armor` is importable, reports host RAM/backend -- call first if unsure.
`dense_armor_clean_signal`	Runs the full Orca shield over a raw series (Orca's own "simple data test" mode, no AI model in the loop). Optional `x_reference`; optional `use_arbiter` for per-point routing.
`dense_armor_detect_anomalies`	Classifies each point as `clean`/`spike`/`regime` without correcting anything -- the routing logic behind `use_arbiter`, exposed standalone.
`dense_armor_robust_filter`	One of the four classic detectors (Chauvenet, Tukey, Hampel, sigma-clipping) or their combined `pressure_valve` orchestrator.
`dense_armor_heal_series`	The neighbor-consensus `healing_filter` -- strong on pervasive noise and genuine sustained jumps, standalone.
`dense_armor_stream_start`	Opens a real-time streaming session (one `MultiChannelStreamingDeviationDetector` kept in server memory) -- for a live sensor stream where waiting to collect a full array first isn't an option. Returns a `session_id`.
`dense_armor_stream_update`	Feeds one real-time reading (one value per channel) into an open session, returns that instant's deviation flag per channel. Call once per real sensor reading, in order -- the detector's state advances with every call.
`dense_armor_stream_end`	Closes a session and frees its state. Sessions are not garbage-collected automatically.

The 3 streaming tools are stateful (session-based), unlike every other tool above, which are pure functions of their inputs. See utility/streaming.py/docs/api/streaming.md for the underlying detector, validated on two independent real physical domains before promotion.

Every tool takes/returns plain JSON (None/null for a missing reading -- converted to/from NaN internally, since raw JSON has no NaN literal). See models.py for the exact input schema of each, or docs/api/arbiter.md / docs/api/orca.md for the underlying functions' full documentation.