Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.
Serves an AI coding agent a searchable index of a codebase's existing capabilities and past incidents, so it can find and reuse what already exists before writing new code. Also exposes blast-radius analysis for shared code and lets the agent reindex the knowledge base, all via local retrieval with no model calls or API keys.
Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.
Enables AI agents to explore, analyze, and search file systems, with tools for directory trees, file statistics, duplicate detection, code counting, and multi-criteria file search.
Provides deep git file-level forensics tools to analyze detailed file histories, changes, and patterns within git repositories, focusing on individual file analysis rather than repository-wide operations.
An MCP server that provides a flexible lens into directory structures and files, enabling LLM clients to efficiently navigate and understand codebases with minimal noise. It offers secure, gitignore-aware file access with tools like directory listing, file reading, and grep-like search.
Enables precise file patching using SEARCH/REPLACE blocks, with mandatory directory sandboxing, automatic Python QA (Ruff/Black/MyPy), and git versioning for rollback.
Enables intelligent handling of large files through smart chunking, search with regex support, line navigation, and streaming capabilities without loading entire files into memory.
Enables read-only auditing of repositories by scanning actual files, flagging risky code, secrets, and untrusted platform configs, and generating Chinese-language reports for human decision-making.
Enables AI coding agents to reverse engineer an operating system from its ISO and rebuild it component by component, exposing 39 tools over stdio for ISO fingerprinting, sandboxed QEMU VM lifecycle, PE analysis, API-surface dumps, syscall tracing, behavior diffs, component scaffolding, compat shims, app-compat testing, and knowledge-base search. It lets an agent survey a reference OS, generate clean-room specs and code, boot the rebuild, and test real applications against it.
Analyzes source code structure across multiple languages using tree-sitter, extracting classes, functions, methods, and metadata with precise line numbers for efficient codebase exploration and AI-assisted development.
Enables Claude Code and AI coding agents to retrieve compact, task-shaped code evidence bundles via MCP, combining AST-aware retrieval, hybrid BM25+FAISS search, call-graph traversal, reranking, and token-budgeted context assembly.
An MCP server giving coding agents context-window-aware code search and safe, atomic multi-file edits — built to cut token usage on large codebases without sacrificing correctness.
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.
Enables semantic code search across codebases with automatic incremental indexing. Searches return relevant code snippets with file paths and line numbers based on natural language queries.
Enables AI assistants to run Offensive360 SAST scans on local codebases, returning security findings with file/line, severity, and fixes, plus scan status tracking.
