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    An MCP server that enables AI-powered IDEs to implement a structured development workflow from requirements gathering to code implementation, guiding users through goal collection, requirements specification, design documentation, task planning, and execution.
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    MIT
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    A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.
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    Enables comprehensive GitHub workflow automation including Actions monitoring, PR management, code search, file operations, and repository management through a code-first architecture that reduces token usage by 98%.
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    AGPL 3.0
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    SSWP MCP — Deterministic software attestation for AI-augmented development. Witness any repo through a 5-gate pipeline, adversarially probe dependencies, and produce self-verifying .sswp.json attestations. Fleet registry across 131 nodes with tamper-proof audit ledger and FTS5 search. Agent-native — one tool call from Hermes, Claude, or Cline.
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    MIT
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    An MCP server designed for React Native and Expo development that provides specialized tools for project scaffolding, architectural best practices, and troubleshooting. It enables AI assistants to guide users through setup, navigation configuration, and CI/CD processes using modern stacks like NativeWind and Zustand.
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    An MCP server providing comprehensive Gitea API coverage with 186 tools for managing repositories, issues, pull requests, and CI/CD workflows. It enables autonomous AI agents to perform complex development and administrative tasks directly through a Gitea instance.
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    MIT
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    A Model Context Protocol (MCP) server for LocalStack that enables management of local AWS development environments, including container lifecycle operations, infrastructure deployments, log analysis with IAM policy generation, chaos injection, and Cloud Pods state management.
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    Apache 2.0
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    An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.
    4