"A search engine platform" matching MCP servers:

An MCP-powered AI platform for satellite mission control, enabling space weather analysis, orbital collision detection, satellite health monitoring, and secure command validation through natural language.

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.

An MCP server that lets AI agents search, save, preview, download, and cart satellite scenes from ISRO's Bhoonidhi portal in natural language, with search and saved queries requiring no login and downloads/cart operations using an out-of-band session.

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.

Enables MCP clients to search, read, and cite the EASA Easy Access Rules with exact rule references, metadata, and linked AMC/GM. It covers aircrew, air operations, SERA, sailplanes, balloons, continuing airworthiness, basic regulation, drones, and aerodromes, returning ranked results and full rule text.

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.

Enables requirement-level question answering over your own ECSS and NASA space standards documents, returning exact citations to the document, issue and requirement ID or an explicit "not covered". It offers tools for listing loaded documents, fetching requirements by ID, keyword-searching requirements and table rows, reading tables, tracing references, comparing a topic across standards, and retrieving section outlines.

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

Enables AI clients to search a bilingual glossary of Earth-Moon space terms by keyword and retrieve individual entries with full definitions, references, and page links through two read-only MCP tools. It covers 720 terms across 8 categories.

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.

Seven keyless Earth-data tools for AI agents: live USGS earthquakes with time-window and magnitude filters, ISS position, current weather, Base gas price, and live-data postcards. One optional paid tool behind a fail-closed spend cap.

Enables an LLM to check S1000D data modules through XML schema validation, BREX rule checks, and reference resolution, returning an evidence-backed combined verification status per data module or across a whole CSDB folder.

Enables LLMs and AI agents to interact with AFSIM through standardized MCP tools for scenario management, entity/component control, simulation execution, and results analysis.

Search 419,000+ LLM-extracted space regulatory filings from the FCC, ITU, UNOOSA, and FAA. Semantic search, entity dossiers, spectrum-band holdings, launch licenses, filing trends, and alerts.

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 users to query live local ADS-B flight data from their own receiver, listing aircraft overhead or near a point and finding aircraft by callsign, ICAO hex code, or registration.

Model Context Protocol server for pyCycle/OpenMDAO engine-cycle workflows, enabling lifecycle management, variable inspection, execution, sweeps, and integration with CPACS aircraft analysis pipelines.

Lets an MCP client read PX4 SITL .ulg logs and answer why a flight failed or underperformed — failsafes, arming reasons, estimator validity versus simulator ground truth, and how late PX4 followed offboard velocity setpoints. It also lists, searches, scans and plots topics, messages and parameters across logs, replacing hand-written pyulog scripts.

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.