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    Provides Dev Container Features that install code intelligence (LSP) and repository knowledge search (Orama) MCP servers into any dev container, enabling coding agents to perform go-to-definition, find references, and hybrid search over project files.
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    MIT
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    Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.
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    MIT
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    Provides GDB and clangd-based tools for debugging C++ programs, enabling step execution, variable inspection, diagnostics, and combined runtime-static analysis.
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    MIT
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    An MCP server that extends AI coding assistants with deterministic, algorithmic capabilities such as code analysis, fault localization, and formal verification, enabling an autonomous engineering team within the IDE.
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    MIT
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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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    A local guardrail MCP server that checks agent-written code against a repo's own patterns — learned statistically from its git history, no LLM.
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    MIT
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    MCP server for verifying AI agent claims vs reality — single-transcript inline grounding-check that flags when an agent's response states facts not in the input context, when its code silently swallows exceptions and substitutes mock data, or when its multi-turn transcript contains contradictions or unverified completion claims. Sub-second, local, free, no API calls.
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    MIT
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    Policy and quality engine for AI coding agents that enforces team coding standards and provides validation gates for agent-assisted software delivery.
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    MIT
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    An MCP server designed for developers to build and manage Minestom-based Minecraft servers by inspecting project environments, build configurations, and API documentation. It enables users to plan features, review design patterns, and discover libraries within the Minestom ecosystem.
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    Apache 2.0
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    Mason indexes your codebase into a persistent concept map linking features and flows to their implementing files, so AI agents can answer "where is X implemented" without running grep/glob. It also provides pre-edit impact analysis and generates CLAUDE.md files from structured analysis of git history, architectural file sampling, and test mappings.
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    MIT
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    Enables LLMs to perform software composition analysis including license detection, vulnerability assessment, SBOM generation, and policy validation using the SEMCL.ONE toolchain.
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    Apache 2.0