"The Models Resource" matching MCP servers:

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

A universal resource server that enables AI models to mount local folders, scrape web pages into Markdown, and query SQLite databases without writing code. It features a real-time TUI dashboard and persistent configuration for managing diverse knowledge sources.

MCP server that integrates Claude with the Kyushu University supercomputer Genkai's TCS (PJM) job scheduler. It enables natural language control of job submission, status monitoring, deletion, resource queries, and reading output files.

MCP server for interacting with Knights of the Old Republic game installations, enabling resource discovery, inspection, and analysis for AI agents.

The Modes MCP Server allows for comprehensive management and control over custom operational modes, supporting full CRUD operations, schema validation, and real-time configuration changes for enhanced mode management.

Implements the Model Context Protocol (MCP) to provide AI models with a standardized interface for connecting to external data sources and tools like file systems, databases, or APIs.

Provides tools for file system operations and text generation using OpenAI-compatible models via the Model Context Protocol, supporting both stdio and HTTP transports.

Enables AI assistants to inspect models, set supported analysis inputs, and request operations from locally installed Bentley Maxsurf applications (Modeler, Stability, Resistance, Motions, Multiframe) through the Model Context Protocol.

A modular framework for building and running multiple MCP servers with file operations, system information, and extensible tool/prompt/resource support.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

MCP server for the Solid protocol enabling AI models to read, write, search, and manage resources in Solid pods.

Enables AI agents to translate text, lists, and local documents through a LibreTranslate instance, detect languages, list supported languages, check service status, and submit translation suggestions over stdio or Streamable HTTP. It also exposes the raw LibreTranslate API through resource, describe, and call tools for broader operations.

Enables AI agents to interact with a sandboxed filesystem via MCP tools for reading, writing, searching, and monitoring files, including batch processing and resource discovery for resume management.

An MCP server providing tools for read-only SQL queries, REST API calls, sandboxed Python execution, and resource lifecycle management, enabling multi-step research and data tasks.

A high-performance TypeScript server that enables interaction with Obsidian vaults through the Local REST API community plugin. It provides comprehensive tool discovery, intelligent resource caching, and unified access to both markdown notes and binary vault files like images and PDFs.

A stateless HTTP streaming MCP server built with Express that provides tools to read resource files (widgetResource.md and pageResource.md) and demonstrates the HTTP Streaming Transport protocol implementation.

Enables AI models to search the web using DuckDuckGo, scrape webpage content in markdown format, and browse/read local files for code analysis and debugging.

A security-hardened MCP server that enables AI models to safely interact with Obsidian vaults through sandboxed file operations and todo tracking. It implements strict validation layers and resource limits to protect the local filesystem from potentially hostile tool calls.

Enables AI clients to control local Windows development tools by exposing project files, code search, file editing, test execution, Git operations, and resource viewing through a secure MCP interface with permission controls.

Enables agents to read, search, batch process, and watch directories of resume files via JSON-RPC MCP methods and resource discovery.