"A search for information related to 'Astro'" matching MCP servers:

Enables AI models to interact with Astro projects by providing runtime information, documentation search, route listing, and integration details.

An MCP server that provides AI assistants with access to Astro documentation, enabling them to search and reference Astro docs when helping users with Astro-related tasks.

Resolves a UI element hash from an Astro dev server to its source file, line, and column, enabling AI agents to edit the exact code.

An MCP server that enables AI assistants to interact with Apache Airflow's REST API for DAG management, task monitoring, and system diagnostics. It provides comprehensive tools for triggering workflows, retrieving logs, and inspecting system health across Airflow 2.x and 3.x versions.

An MCP server that automates the migration of WordPress sites into production-ready Astro projects by extracting content, SEO, and ACF data via REST API. It converts HTML to clean Markdown and scaffolds complete project structures through conversational commands in Claude Code.

Enables automated blog post creation and management for Astro blogs deployed on Vercel through GitHub integration. Supports secure API-based operations with automatic deployment triggers and integration with automation tools like n8n.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

MCP server for AI agents to send notifications, copy to clipboard, request confirmations, and collect text input from users across their devices.

Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.

Enables turning requirements documents into RFCs, functional test scenarios, and a traceability matrix, with tools to parse, diff, and verify coverage.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

A Model Context Protocol server that converts Markdown content to HTML format.

Enables an MCP-compatible AI client to author Blender Python, run Blender, import the resulting asset into Unity with materials and a prefab, place it in the active scene, verify the import, and revise the same asset using stable IDs.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Provides over 1,000 creative ways to decline requests across four categories (polite, humorous, professional, and creative). The MCP server wraps a REST API to help users craft professional rejections through natural language interactions.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Generates standalone, human-readable MCP servers from RAML 1.0 or OpenAPI 3.x specs, exposing one tool per resource and method that makes real HTTP calls. It understands Mule/Anypoint auth patterns such as Client ID enforcement and OAuth2, wiring the expected credentials from environment variables at call time.

Turns any OpenAPI REST or GraphQL API into an MCP server, allowing AI assistants like Claude to interact with APIs without writing code.

Transforms Figma design data into a compact, LLM-friendly format for code generation, reducing size by 99.5% while preserving UI-critical information.

Enables existing Express routers to be exposed as MCP tools, dispatching tool calls through the Express middleware stack in-process without a network hop.