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Enables AI assistants to interact with GitHub repositories, issues, and pull requests via the Model Context Protocol.
Connects AI assistants to the TON blockchain through a Model Context Protocol (MCP) server.
Enables Docker container management through natural language using a custom GPT interface.
Enables accessing and managing Langfuse prompts through the Model Context Protocol (MCP).
Facilitates multi-agent conversation protocols for the Google Resource Settings API.
Facilitates the creation of MCP servers using Node In Layers framework.
Performs real-time security scans on codebases for AI agents and LLM-powered IDEs.
Checks if provided context is safe by identifying potential code injection or harmful content.
Experiment with Anthropic's Claude and the Model Context Protocol (MCP) through an interactive web interface.
Provides AI clients the ability to remember information about users across conversations using vector search technology.
Provides a foundational template for developing hot-reloadable Model Context Protocol servers.
Builds and runs polyglot Model Context Protocol (MCP) servers for AI agents.
Deploys a Model Context Protocol (MCP) server and integrates it with Microsoft Copilot Studio to provide contextual information to large language models.
Provides a standardized Model Context Protocol (MCP) interface for accessing Repology package repository data.
Exposes PostgreSQL databases as Model Context Protocol resources and tools via HTTP and Stdio transports.
Integrates TikTok data access into AI models and applications via TikNeuron.
Enables programmatic access to Meta Ads data and management features through a Model Context Protocol (MCP) server interface.
Integrate AI assistants with Metabase analytics data, bridging your analytics platform with conversational AI.
Provides structural code intelligence to AI agents by understanding project structure and performing AST-based searches on local repositories.
Generate and query privacy-preserving synthetic health data that is FHIR-compliant and differentially private.
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