Discover +718 AI Agents apps & tools
Pros: Uses official language server data to avoid hallucinated symbol relationships. Supports offline LSIF dumps for semantic retrieval without live servers. Connects to LSP via stdio, TCP, or Unix sockets. Manages multiple language servers within one workspace.
Cons: Pre-v1 status may affect production stability. Requires Go and an MCP-compatible client to install. Depends on available LSPs or LSIF indexes per language.
Pros: Direct protocol access to Hot Pepper Gourmet search data. Exposes specific endpoints like search_shops and list_genres. Quick installation via Homebrew or npx. Designed for MCP hosts, reducing custom prompt work.
Cons: Requires a valid Hot Pepper Gourmet API key to operate. Scoped to a single national restaurant dataset. Community-developed and not affiliated with the API owner. Depends on an MCP host application to deliver results.
Pros: Local-first storage keeps project secrets on the user's machine. MCP server provides direct integration for AI clients. Desktop application and CLI for visual and terminal management.
Cons: Requires Node.js 22+ and pnpm for source installation. Best suited to developers and power users, not casual users. Handoff effectiveness depends on agent-side integration and mapping.
Pros: MCP compatibility, proven with Claude Desktop and Cursor integration. TypeScript CLI enables installation via 'npx @chewcw/tia-portal-openness-mcpserver install'. Open-source project with active maintenance and 100-plus commits.
Cons: Requires licensed TIA Portal installation on Windows hosts. Depends on .NET Framework 4.8 runtime on the host. Generated PLC code needs human verification and test compilation.
Pros: Git-aware workflow tracks upstream and local skill changes. Single source of truth for skill configurations across platforms. MCP server browsing, import, and editing in one workspace. Syncs skills with Claude Code and GitHub Copilot integrations.
Cons: Requires MCP-compatible environments to be fully useful. Value depends on established Git and repository practices. Targeted at developers, not aimed at non-technical users.
Pros: Lists environment variables and paths for verifying server context. Built-in connectivity probes that reveal handshake and transport issues. Enumerates registered tools and resources available to the model. Open-source repository on GitHub for inspection and contribution.
Cons: Findings reflect only the host where the extension runs. Primarily intended for development and not long-term monitoring. Requires a Python environment and an MCP-compliant client.
Pros: Automated hardware detection for NVIDIA, AMD, and Apple Silicon. Zero-config deployment of inference engines such as Ollama and vLLM. Offline-first design keeps core management available without internet. Cross-platform support for Linux, macOS, and Windows.
Cons: 61 MCP tools create a notable initial learning curve. Automated setups should be validated before production use. Focused on local self-hosting, less aimed at cloud-only teams.
Pros: Adds image outputs to text assistants via the Model Context Protocol. Can be launched quickly with npx for rapid testing. Accesses a large template catalog through an image generation service.
Cons: Relies on an external image API, sending requests off-host. Requires Imgflip username and password as environment variables. Geared toward developers; not targeted at non-technical end users.
