"Spaceflight API - Space Launch and Mission Data" matching MCP servers:

Color conversions math for 205+ color spaces, including OKLCH, CIELAB, CAM16, HCT, Display P3, Rec. 2020, camera logs, print and appearance models. Every space is verified to an authoritative reference. A color goes in as any CSS string or as channel values. Tools: convert, gamut, css, space, spaces, cube, shader, icc and code. No API key, no configuration and no network.

Enables checking a public website before launch and returning a scored pass, warn, or fail report with a fix for every problem found.

Enables an AI agent to screen-record a live Expo app on a connected phone, auto-plan and direct the footage with captions, motion graphics, pop-outs and sound, then export a 1080p MP4. It also opens an interactive studio where users refine scenes by dragging, resizing, or typing prompts, and exposes all app_* control tools from expo-mcp-bridge in the same session.

Scores MCP servers for launch-readiness across trust, tool UX, docs, observability, marketplace fit, and partner narrative, and generates partner briefs and launch path comparisons.

Enables reading and authoring of Adobe Experience Platform Tags resources—properties, rules, data elements, extensions, libraries, and environments—via the Reactor API, with configurable write modes for safe or dangerous operations.

Enables AI agents to resolve stored API tokens and credentials from a locally encrypted vault through an inject_secret tool, writing them to env-file handles the agent consumes outside the conversation so secret values never appear in chat transcripts. Provides listing and metadata access while requiring a human unlock gate before any decryption.

Manages the lifecycle of MCP services with health monitoring, auto-restart capabilities, and process tracking. Enables users to start, stop, restart, and monitor MCP services through natural language commands.

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to interact with generated REST APIs by discovering and using CRUD tools and read-only data resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Enables AI assistants to interact directly with generated REST APIs by exposing CRUD operations as MCP tools and read-only data as resources.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Exposes every generated CRUD service to LLM clients such as Claude, ChatGPT, or Cursor through discoverable MCP tools for create, update, and list actions plus read-only resources, so models can query and manipulate data without raw HTTP calls. It runs alongside the same codebase's REST endpoints, letting clients and AI share one data layer.

Enables LLMs to discover and invoke generated CRUD operations as MCP tools and read data through MCP resources, alongside standard REST endpoints, using one codebase.

Enables LLMs to discover and invoke generated CRUD tools and resources, allowing AI assistants to interact with the same data exposed by the REST API. Supports MCP clients such as Cursor and Claude over HTTP.

Enables LLMs to discover and invoke generated API actions as MCP tools and read context through MCP resources for list and get-by-id operations. It provides HTTP-based MCP access for clients such as Cursor and Claude without extra glue code.

Enables LLMs to interact directly with generated REST APIs by exposing CRUD actions as MCP tools and read-only data as MCP resources.

Enables LLMs and MCP clients to discover and use generated CRUD services through MCP tools and resources, exposing actions like create, update, delete, and read-only list/get access alongside REST endpoints.

Enables AI assistants to discover and invoke generated CRUD tools and read-only resources, letting them interact directly with REST-backed services built on Express, Mongoose, or Sequelize.

Enables LLMs and AI assistants to discover and use tools and resources for CRUD operations on generated services, providing direct interaction with the same data exposed by REST endpoints.

Enables LLMs to discover and invoke CRUD tools and read-only resources for generated services, allowing direct interaction with the same data exposed by the REST API.

Enables LLMs to interact with generated REST API services by exposing MCP tools and resources for CRUD operations, listing, and retrieval. It lets AI clients discover and call create, update, delete, list, and get-by-id actions against the same data available over REST.

Exposes every generated Express service to LLM clients over HTTP as MCP tools (create, update, delete actions) and resources (list and get-by-id context), so AI assistants can discover and manipulate application data without raw HTTP calls. This lets teams run a single codebase that serves both traditional REST clients and AI agents such as Cursor or Claude.

Enables LLMs to interact with generated CRUD services through MCP tools and resources, so they can create, update, list, and fetch records via a standard protocol. It also provides REST API access from the same codebase for traditional clients.

Enables LLM clients such as Claude, ChatGPT, and Cursor to discover and act on generated CRUD services over HTTP through tools like create, update, list, and delete, plus read-only resources for listing and fetching records by ID, with no extra glue code.

Enables LLMs to discover and invoke generated API operations as MCP tools and resources, supporting create, update, list, and get-by-id actions over the same data exposed by the REST API.

Enables LLMs to discover and use generated REST API services as Model Context Protocol tools and resources, supporting CRUD actions and read-only data access without custom glue code.

Enables LLMs to interact directly with generated REST services by discovering and invoking CRUD tools and reading exposed resources for list and get-by-id data.

It enables AI assistants to discover and invoke CRUD operations as MCP tools and read application data through MCP resources, allowing natural-language interaction with the generated backend.

It enables LLMs to interact with generated REST APIs by exposing CRUD operations as MCP tools and readable records as MCP resources. This lets AI clients discover and manage API data through the Model Context Protocol.

Enables LLMs to discover and invoke CRUD operations and read-only data resources for generated services through the Model Context Protocol, alongside traditional REST endpoints.

Enables AI assistants to discover and invoke generated CRUD actions as MCP tools and read-only data as MCP resources, so they can interact with REST APIs through a standard protocol.

MCP server generated from an OpenAPI Specification, exposing tools via HTTP. It allows clients to invoke API operations using JSON-RPC 2.0 requests.

MCP server that enables AI agents to read and write records in a live REST API generated from pasted data, with tools for listing, querying, inserting, updating, and deleting records.

Enables LLMs to make arbitrary HTTP API requests with any method and full control over headers, query parameters, and body.

An MCP Server that enables interaction with Google's Data Labeling API, allowing users to manage datasets, annotations, and labeling tasks through natural language commands.

An MCP Server that provides a conversational interface to the UK Open Banking account information API, allowing agents to interact with bank account data through natural language commands.

This MCP server enables users to interact with AWS Amplify Gen2 application data through natural language, allowing AI assistants like Claude to perform operations on Amplify data models using conversational language instead of complex code.

Enables Claude to create local, persistent mock REST APIs on demand from plain-language descriptions, with data written to disk so it survives restarts.

Serves the Pancake API documentation to AI assistants, enabling them to search endpoints, retrieve full API contracts, webhook payloads, and guides without live API access.