"Using RAG to Provide Documentation to LLMs" matching MCP servers:

Wraps the Launch Library 2 API to provide access to launch data, enabling queries about upcoming and past launches.

MCP server that wraps the NASA Space-Track API to provide tools for TLE lookup, conjunction query, and satellite catalog search.

Provides AI assistants with live information about who is currently in space and the location of the ISS, using the free Open Notify API.

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.

Provides deterministic computation of magnetic declination and sky positions (sun, moon, planets) for any location and time, enabling agents to get accurate ephemeris and geomagnetic data without hallucination.

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.

US drone airspace intelligence and mission planning. 24 MCP tools: check airspace restrictions across 11 FAA data layers, plan grid/orbit/corridor surveys, validate Part 107 compliance, generate pre-flight briefings, and export to KML/GPX/QGC/Litchi/WPML.

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.

Connects AI coding agents to Microsoft Flight Simulator via SimConnect, enabling full read/write access to SimVars, L-vars, events, calculator code execution, and embedded documentation for add-on development.

Provides smart URL navigation to ANSYS Fluent documentation, enabling AI assistants to quickly find relevant help pages and manual sections.

Enables an LLM to build S1000D data modules and publication modules from structured drafts and numbering rules instead of writing XML, verifying results against XSD, BREX and references and applying only unambiguous fixes. It also supports converting experiential manuals, extracting and restoring translatable segments, creating comment modules, and writing nothing by default.

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.

Enables AI agents to identify and diagnose ArduPilot flight controllers over MAVLink, reading status, parameters, backups and diffs, and running guided calibrations. It is read-only by default, with human-approved, read-back-verified parameter changes and identity-checked backups gated behind explicit flags.

Enables an LLM to read S1000D BREX data modules and check data modules against them, reporting allowed elements, values, SNS code meanings, and permitted illustration formats. Also returns each rule violation with its XPath location, actual value, rule ID, and severity, following the BREX reference chain layer by layer.

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.

This server allows Large Language Models to consume S1000D data and publication modules as structured information instead of raw XML. It enables reading the content's hierarchy, applicability to specific products, and document verification status.