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    Serves token-efficient code context for AI coding assistants by exposing Python signatures, call graphs, and budget-packed task context as MCP tools, enabling precise codebase queries without reading entire files.
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    MIT
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    Helps AI assistants optimize Linux workloads on Arm64 by parsing perf report output, recommending NEON SIMD intrinsics for hot loops, and auditing Python dependency manifests for arm64 wheel availability — all offline and structured.
    4
    Apache 2.0
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    Enables coding agents to perform safe, project-wide Python refactoring (rename, move, extract, inline, change signature, organize imports, etc.) with a dry-run safety contract and LSP-coordinate addressing.
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    MIT
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    Enables AI assistants to understand and navigate codebases through structural analysis. Provides code mapping, symbol search, and impact analysis using ast-grep for accurate parsing of Python, JavaScript, TypeScript, and Go projects.
    4
    52
    MIT
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    Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.
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    10 npm
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    MIT
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    Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.
    22
    45 npm
    4
    Apache 2.0
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    Deterministic navigation maps over code AND markdown for AI agents: one MCP server, two lenses (tree-sitter TS/JS/Python + markdown). Map a whole project, read one function or one doc-section, ~99% less context. Merges codelens + docslens.
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    AGPL 3.0