Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.
Enables cross-repository architecture discovery and dependency routing across multi-service codebases, with automated scanning and batch AST indexing for AI agents to trace request lifecycles and navigate service boundaries.
Enables AI agents to discover open-source libraries worth reusing through multi-signal repository ranking and to search real code with sub-50ms local regex/semantic queries federated to live GitHub, grep.app, and package-registry backends. It exposes seven tools over stdio—repo search, code search, repo profile/tree, file and docs fetching, and a usage guide—with structured filters, typed pre-execution errors, and a byte-identical CLI twin.
Enables coding agents to search, inspect, and recommend open-source Odoo/OCA modules before writing custom code, with source-backed evidence and version/migration guidance.
Enables scanning Java (Maven) projects for known vulnerabilities via natural language, returning severity-sorted reports with CVSS scores and fix versions.
Provides a memory server for coding agents over MCP, capturing and retrieving the meaning and rationale behind code decisions down to the function level. It enables agents to search product memory, get task context, and propose new memories to avoid re-deriving or re-breaking past decisions.
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.
Enables exhaustive, unit-by-unit analysis of open-source repositories through MCP, cataloging every file and symbol, resolving dynamically built routes and URL paths, and mapping call and value flows via LSP and Joern. Every conclusion carries file:line evidence tied to a pinned commit, with unresolved items tracked in an unverified list and assigned verification grades L0–L2.
Enables AI agents to analyze LookML repositories for dead code, PDT costs, schema drift, and migration impact, using offline-first analysis of Looker and BigQuery metadata.
Exposes CodeScene's Code Health analysis as local AI-friendly tools, enabling AI assistants to provide insights on code quality, maintainability, and technical debt directly from the codebase.
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 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.
