#code-intelligence

MCP server for AI coding agents - gives Claude Code, OpenCode, Cursor, and Codex CLI structural understanding of your codebase: symbol graph, blast radius, semantic search, conventions.

Full AI context and content layer for coding agents over one MCP server — tree-sitter code-map, document RAG, shared memory, multi-agent comms, web crawl, git history + blame. 300+ languages, 10+ agent harnesses, pure Rust.

A memory of the codebase your AI agent can't get from grep: a persistent call graph, semantic search, and per-symbol review learnings over any repo. Self-hosted MCP server — Go, tree-sitter, Apache AGE.

The repo map your coding agent is forced to use — ~98% fewer tokens (up to 99.9% per task) to understand a TS/JS codebase. PageRank hubs, Aider-style symbol ranking, token-budgeted digest, --any router, wired into the agent loop.

Cut AI-agent token waste ~90% — query a local SQLite structural index of your JS/TS/CSS codebase with SQL in one round-trip instead of 3–5 file reads. Symbols, imports, calls, components, CSS tokens, coverage, markers. CLI, MCP (21 tools), HTTP, GitHub Action, ESM API. 71 recipes; AST+resolver; SARIF/audit/baselines for CI.

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Code research platform for AI agents: local + GitHub search, LSP semantics, AST patterns, and compact context. one toolset as MCP or CLI

Codex-native codebase intelligence: deterministic repo context, change-plan drift review, and verification gating for AI coding agents. Local-first, zero API keys.

MCP server that orchestrates language servers into agent-native workflows. 65 tools, 30 CI-verified languages.

Rust-native codebase intelligence for Dart and Flutter.