"Creating a Document as a Knowledge Base" matching MCP servers:

A read-only MCP server over the Cassini-Huygens mission dataset exposing tools for querying activity data such as listing, searching, counting, aggregating, and timeline analysis.

Connects AI agents to MAVLink drones (PX4, ArduPilot) via a capability layer with safety guardrails, enabling telemetry, flight control, and mission management through natural language.

Enables AI agents to run Link Budget, Radar Range, and EO/IR calculations and to read the user's saved projects by exchanging request and result files through a local folder that the macOS/iPadOS engineering calculator app watches. All computation happens in the app, and results are returned verbatim, including undetermined outcomes that report missing inputs.

A local, open-source MCP server that wraps astronomy data services (SIMBAD, JPL Horizons, VizieR, etc.) behind a conversational interface, enabling AI assistants to answer astronomy questions with real, typed, and provenance-tracked data.

Vedaksha MCP Server Astronomical ephemeris and Vedic astrology computation for AI agents via the Model Context Protocol.

Enables AI agents to autonomously write, run, and debug NASA GMAT mission scripts, with tools for script execution, curated knowledge of common pitfalls, and semantic documentation search.

Provides LLM agents with a toolset for reviewing S1000D technical publications, including XML schema validation, cross-reference and applicability checks, skeleton generation, and AI-suggested fixes.

Enables authenticated access to a pilot's flight-planning account, letting an assistant search installed airfield charts offline, pull full airfield records with pilot notes and live feedback, and list, read, or download flightplans from local disk or cloud storage.

Enables external agents to define and execute read-only derived properties over an ontology by analyzing field requests, validating link cardinality, and generating Semantic Join SQL within a three-hop neighborhood.

Viznoir empowers AI agents to directly analyze and visualize complex 3D scientific and engineering data in cinematic resolution using a fully headless VTK engine. It enables LLMs to seamlessly execute advanced physical analysis, 3D rendering, and generate publication-ready animations purely through natural language commands.

Provides standardized access to 20+ NASA data sources including astronomy pictures, Mars rover photos, near-Earth objects, satellite imagery, space weather, and planetary data through a unified interface optimized for AI consumption.

Generates a unique number derived from the International Space Station's current orbital location, useful for space-derived identifiers or deterministic random seeds.

Enables retrieval of satellite vegetation statistics (NDVI) and searches of Sentinel-2 scene catalogs, returning results as JSON.

Enables autonomous satellite agents to communicate and coordinate for orbital operations such as collision avoidance, maneuver planning, and mission execution.

Provides satellite tracking data using Two-Line Element sets from a free public API. Enables querying satellite orbits and positions through natural language or direct tool calls.

Provides real-time space data including ISS and Tiangong tracking, crew in space, rocket launches, space news, Mars missions, a star catalog, near-Earth asteroids, and satellites. All 18 tools are read-only.

Provides live astronomical telemetry and space data, including ISS tracking, NASA APOD, and solar weather, via a Model Context Protocol server with SSE and JSON-RPC support.

Aggregated public space data — satellite orbits, catalogs and launches — as a single AI-friendly CLI.

Enables AI assistants to answer live space weather questions by exposing NOAA SWPC data as callable tools for the current Kp index, 3-day geomagnetic forecast, GOES solar flare activity, and aurora visibility at any latitude.

Spacecraft component specifications read from vendor datasheets (2,500+ products, 24 categories): search by engineering requirements, compare, budget mass and power, read datasheet digests and facts, find data gaps, and assemble a bus around parts you lock. Every value cites its datasheet page; missing data is named, never guessed.