nina-mcp

An MCP server that gives an AI agent (your openclaw agent, Claude, or anything else that speaks MCP) control over N.I.N.A. (Nighttime Imaging 'N' Astronomy). Mount, camera, sequencer, filter wheel, focuser, rotator, dome, guider, switches, flat panels, and target scheduling are all fully implemented. Safety monitor and weather stations are exposed as status reads.

It talks to NINA over HTTP via the community ninaAPI plugin (also called "Advanced API" in the NINA plugin list) — this MCP server doesn't replace that plugin, it wraps it.

Prerequisites

1. NINA installed and running, with the Advanced API plugin installed (NINA > Plugins > search "Advanced API") and enabled.

2. Note the host/port it's listening on (NINA > Options > Plugins > Advanced API — default port is 1888). If this MCP server runs on the same PC as NINA, the default 127.0.0.1:1888 just works.

3. Python 3.10+ wherever this MCP server runs.

Related MCP server: MCP TypeScript NASA Server

Install

cd nina-mcp
python -m venv .venv
# Windows: .venv\Scripts\activate
source .venv/bin/activate
pip install -e .

Copy .env.example to .env and adjust NINA_HOST / NINA_PORT if needed (defaults assume NINA is on the same machine, default port).

Run it

Directly, for any MCP client that spawns servers over stdio (Claude Desktop, Claude Code, and presumably your openclaw agent — MCP's stdio transport is just "run this command, talk JSON-RPC over its stdin/stdout"):

nina-mcp
# or: python -m nina_mcp.server

Point your MCP client's config at it. The generic shape (adjust keys to whatever openclaw's config format expects — this is the same shape Claude Desktop uses):

{
  "mcpServers": {
    "nina": {
      "command": "/absolute/path/to/nina-mcp/.venv/bin/python",
      "args": ["-m", "nina_mcp.server"],
      "env": {
        "NINA_HOST": "127.0.0.1",
        "NINA_PORT": "1888"
      }
    }
  }
}

If openclaw needs a network transport instead of stdio (SSE / streamable HTTP), FastMCP supports that too — change the mcp.run() call in server.py to mcp.run(transport="streamable-http") (see the mcp Python SDK docs for the current options; this changed a couple of times across versions).

Testing without an agent

test_client.py is a minimal MCP client included so you can sanity-check the server without wiring up openclaw first. It spawns the server itself over stdio, same as a real client would:

python test_client.py                                  # list all tools
python test_client.py nina_mount_info                   # call with no args
python test_client.py nina_mount_sync ra=10.5 dec=41.2   # call with args

What's actually implemented

Core (full control)

• Equipment (generic, tools/equipment.py) — combined status, list available devices, connect/disconnect/rescan any device by name, and poll recent event history. Connect equipment through these tools before using the device-specific ones below.

• Mount (tools/mount.py) — info, home, park/unpark, tracking mode, slew (raw, center, or center+rotate), stop slew, sync, meridian flip, set park position.

• Camera (tools/camera.py) — info, capture (all the knobs: duration, gain, save, target name, image type, plate-solve, binning, readout mode), abort exposure, capture statistics, cooling/warming, dew heater, USB limit.

• Sequencer (tools/sequencer.py) — get sequence JSON/state, start, stop, reset, skip, edit a field in place, list/load saved sequences by name, load a full sequence from JSON, update a target's coordinates.

Target Scheduler — read this before assuming "full control"

ninaAPI does not expose Target Scheduler's projects/targets/exposure plans as REST endpoints. I checked ninaAPI's source directly (WebService/V2/Application/TargetScheduler.cs): all it does is forward three of TS's internal status messages (wait-start, new-target-start, target-start) as read-only events. There's no endpoint to list projects, change priorities, or ask what it'll image next — that logic is entirely internal to the Target Scheduler plugin's own Planning Engine, which only runs from inside a NINA sequence via its "Target Scheduler Container" instruction (which you still have to build once, by hand, in the NINA UI — no API creates it for you either).

So tools/target_scheduler.py gives you the two things that actually exist:

1. Running it — just a normal sequence. Build a sequence containing a Target Scheduler Container instruction in NINA's UI once, then use nina_sequence_load_by_name + nina_sequence_start to run it.

2. Reading/editing its data — Target Scheduler stores everything in a local SQLite database (schedulerdb.sqlite, path documented in the plugin's own docs). Rather than hardcode column names I couldn't verify against a real database (the schema changed across major TS versions), ts_db.py implements generic, schema-validated tools: ts_list_tables, ts_describe_table, ts_read_table, and a narrow ts_update_cell (single column, single row, by id) that's disabled by default — set TS_ALLOW_WRITES=true once you've backed up your database and want an agent editing it. ts_recent_events polls the live status events from (1).

To support Target Scheduler automation, we have implemented typed database write tools (ts_set_project_priority, ts_set_project_enabled, and ts_toggle_target_enabled) that dynamically discover the active tables and columns (supporting both TS4 and TS5). ts_recent_events polls the live status events from (1).

Status-Only Modules (tools/placeholders.py)

Safety monitor and weather stations do not have REST control surfaces upstream, so they only expose *_info status reads.

Architecture

src/nina_mcp/
  config.py           settings from environment variables
  nina_client.py       async HTTP client, unwraps ninaAPI's response envelope
  ts_db.py              generic, schema-validated SQLite access for Target Scheduler
  server.py             FastMCP app, registers every tool module
  tools/
    equipment.py         generic connect/disconnect/list/rescan/event-history
    mount.py             full mount control
    camera.py            full camera control
    sequencer.py         full sequencer control
    target_scheduler.py  DB + event-based TS tools (with schema discovery)
    filterwheel.py       filter wheel control
    focuser.py           focuser movement and configuration
    rotator.py           sky rotator control
    dome.py              observatory dome control
    guider.py            autoguiding control
    switch.py            switch/relay control
    flatdevice.py        flat panel illumination and cover control
    placeholders.py      status-only equipment (weather, safety monitor)
test_client.py          minimal standalone MCP client for manual testing

Every endpoint path and query parameter here was confirmed against ninaAPI's own C# source (WebService/V2/...), not guessed from third-party docs — I pulled the repo and grepped the [Route(...)] attributes directly. The one thing that genuinely doesn't exist upstream is Target Scheduler's REST API, which is why that module takes a database-directed approach.

Safety notes

• This gives an agent real control of a telescope mount and camera. A clumsy nina_mount_slew or an unattended nina_sequence_start can point expensive optics somewhere bad or burn a night's imaging. Consider keeping a human in the loop for anything that moves hardware until you trust the setup.

• TS_ALLOW_WRITES is off by default for a reason — back up schedulerdb.sqlite before turning it on.

• nina_camera_capture's omit_image defaults to True to avoid dumping large base64 blobs into an agent's context by default.