• Gen AI apps are built with MongoDB Atlas Icon
    Gen AI apps are built with MongoDB Atlas

    Build gen AI apps with an all-in-one modern database: MongoDB Atlas

    MongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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    Protect your business with AI policies and data loss prevention in the browser

    Make AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
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  • 1
    MCPTools

    MCPTools

    A command-line interface for interacting with MCP

    mcptools is a command-line interface designed for interacting with Model Context Protocol (MCP) servers using both standard input/output and HTTP transport methods. It allows users to discover and call tools, list resources, and interact with MCP-compatible servers. The tool supports various output formats and includes features like an interactive shell, project scaffolding, and server alias management. ​
    Downloads: 10 This Week
    Last Update:
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  • 2
    GitHub MCP Server

    GitHub MCP Server

    GitHub's official MCP Server

    ...The server defines tools and resources with fine-grained scopes, leaning on GitHub’s auth to enforce least privilege and auditable access. It supports both stdio and HTTP transports, enabling IDE and headless integrations, and adopts common MCP behaviors like prompts, schemas, and tool definitions to keep agent calls predictable. Documentation covers setup, tokens, and client configuration, highlighting native editor integrations. Its design goal is to give AI agents first-class, governed access to GitHub workflows.
    Downloads: 3 This Week
    Last Update:
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  • 3
    HttpRunner

    HttpRunner

    Testing framework that began with API and performance testing

    ...The current major version is implemented in Go, with the legacy Python edition split to a separate repository; this shift emphasizes a single, fast, cross-platform runtime for modern pipelines. It provides declarative test cases, data-driven parametrization, and plugin mechanisms so teams can compose reusable steps and validations at scale. Beyond HTTP(S) APIs, the ecosystem spans UI automation (via a companion UI extension), load testing, and integrations that turn the framework into a one-stop solution for functional and performance needs. The repo includes examples, a migration path from older versions, and badges for CI health and coverage to encourage production adoption. Its community presence and long history (since 2017) make it a common choice in Chinese and global QA teams looking for an all-in-one runner.
    Downloads: 0 This Week
    Last Update:
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