Discover +1424 AI apps & tools

Pros: Property-based search for locating specific entities. Persistent local storage keeps the graph under user control. Built in TypeScript with an extensible architecture. Designed as an MCP server for host integration.

Cons: Requires an MCP host such as Claude Desktop or MCP Inspector. Needs cloning and TypeScript build, not plug-and-play for non-developers. Model output still requires human verification for high-stakes content.

Pros: Direct integration with Nmap, Dig, Whois, Curl, and SQLMap for agent access. Implements the Model Context Protocol for compatibility with MCP clients. Docker-ready deployment for reproducible environments. Open-source codebase allows adding custom command-line tools.

Cons: Automated commands require human validation before operational use. Some scans need elevated privileges, increasing deployment complexity. Results depend on underlying CLI tools and network conditions. Designed for MCP clients; non-MCP workflows require adapters.

Pros: Direct AI-to-Figma bridge removes manual copy-paste of design data. Exposes pages, layers, components, and node properties for inspection. Open-source repository allows community review and contributions.

Cons: Requires MCP-compatible client and developer setup knowledge. Depends on Figma API responses and rate limits for freshness. Current implementation is read-only, not designed to edit files.

Pros: Integrates directly with MCP hosts for in-pipeline humanization. Open-source codebase available for audit and modification. Callable as a function during model generation for automation.

Cons: Requires an MCP-compatible host to operate. Intended for technical users familiar with server configuration. Effectiveness varies with source model and humanization settings.

Pros: Native MCP implementation works with Claude Desktop and Cursor. Direct API access eliminates CSV or JSON export steps. Supports project-based localization and multi-locale management.

Cons: Requires an active Sift account and valid API key. Node.js required for installation and runtime. Automated translation updates need human review before release.

Pros: Native MCP support for direct model-to-log access. Regex-capable search for precise error and pattern filtering. Efficient file handling for very large logs without full memory loads. Open-source MIT license enables code review and extension.

Cons: Requires an MCP host and a Node.js runtime to deploy. Designed for technical users, not non-technical operators. Real-time tailing depends on the MCP host's setup and connectivity.

Pros: Open-source codebase allows community review and contributions. Supports Sublime Text 3 and 4 on Windows, macOS, and Linux. Exposes editor content and project metadata to MCP workflows.

Cons: Requires an external MCP-compliant server to function. Server connections configured via JSON, needing manual edits. No bundled AI model; models run on external servers.

Pros: Native Model Context Protocol integration for MCP-compatible clients. Open-source design permits inspection and customization of processing logic. Processes text in the user environment for improved data control. Lightweight, modular Node.js service suited to developer workflows.

Cons: Requires an MCP host and Node.js, limiting non-developer adoption. Output quality depends on the connected AI model's language capabilities. Connected AI client typically needs internet for inference processing.

Pros: Native Model Context Protocol support for MCP-compatible hosts. Open-source MIT license allows code inspection and customization. Modular servers can be deployed individually to match workflows. Runs on TypeScript/Node.js across Windows, macOS, and Linux.

Cons: Connectors require API keys or tokens for third-party services. Deployment requires Node.js runtime and routine rebuilds. Positioned for early adopters; expects technical configuration and ops familiarity.

Pros: Accesses a bibliographic index with over 200 million records. Provides citation lists and author publication lookups inside chat. Open-source codebase available on GitHub for audit. Installs via npm/npx and integrates with MCP hosts.

Cons: Full-text PDFs not guaranteed; depends on open access or permissions. Potential rate limiting without a Semantic Scholar API key. Requires an MCP-compatible host and Node.js v18 or higher.

Pros: Integrates with the Model Context Protocol for MCP-compatible clients. Provides a callable formatting endpoint for explicit text transformations. Runs on Node.js and supports local or container deployment. Open-source codebase enables customization and community contributions.

Cons: Requires an MCP host such as Claude Desktop to operate. Formatting depends on the connected model’s responses and prompts. Needs a Node.js runtime, aimed at developer workflows. Not aimed at non-technical users without integration effort.

Pros: Exposes Pi-hole API as MCP tools for AI-driven queries and commands. Supports timed disabling of blocking as a callable operation. API tokens are handled via environment variables for local authentication. Open-source implementation aimed at easy deployment.

Cons: Requires an MCP-compatible host and Node.js environment. Intended for technical users familiar with local server setup. Accuracy of outputs depends on the Pi-hole instance health and network reachability.

Pros: Enables byte-range reads so models access specific segments of large files. Written in Go, offering low resource overhead when streaming files. Runs locally as an MCP server, keeping files off third-party cloud storage. Compatible with any MCP host, including Claude Desktop.

Cons: Requires MCP host and manual configuration, challenging for non-technical users. Search results are most effective on UTF-8 text, limited on binary files. Model interpretations of returned bytes require human verification.

Pros: Access to over 200 biomedical ontologies. MCP support enables LLMs to call ontology lookups. Graph visualization of term hierarchies via Neo4j. Dockerized deployment option for private hosting.

Cons: Public instance enforces rate limits for high-throughput querying. Machine-returned mappings need expert validation for contested terms. Local deployment requires configuration and maintenance. Graph queries may need familiarity with Neo4j for advanced use.