Red Hat's zero-dependency C++23 code-context engine — ranked call graphs and blast-radius analysis, indexing a repo in under half a second with no server and no database.
GraftMCP Server
Summary
Builds a searchable markdown graph of your repo so coding agents stop re-exploring it on every task — reported 42% fewer tokens and 46% fewer tool calls.
Features
- Per-symbol code graph with tree-sitter summaries and crux excerpts
- Plain-markdown cache: regenerable, diffable, in sync with uncommitted work
- Six MCP tools for lookup, call tracing and repo mapping
- 23 languages; monorepo, submodule and multi-repo layouts
- Reported 42% token savings and 46% fewer tool calls
Installation
Set up this MCP server in your favorite AI agent — copy a ready-made configuration below.
Description
Every task, a coding agent starts blind. It greps a term, opens a file, follows an import, backs out and tries again — paying for the same reconnaissance on every single request. Graft is an open-source context layer that does that exploration once, ahead of time, and keeps the result.
It builds a per-symbol code graph of the repository as a folder of linked markdown files: summaries, "crux" excerpts extracted with tree-sitter, and the call and import edges between them. Because the cache is plain markdown on disk, it is regenerable, diffable and readable by a human, and it stays in sync with the working tree rather than the last commit — uncommitted changes are included. Twenty-three languages are supported, among them TypeScript, Python, Go, Java and Rust, and the layout handles monorepos, submodules and multi-repo folders.
The MCP server exposes six tools — graft_find_code, graft_file_api, graft_trace_calls, graft_find_all, graft_repo_map and graft_check_freshness — so an agent can ask where something is defined, trace who calls it, or pull a map of the repository without reading the files. graft init also wires the graph directly into Claude Code and other agents, and an interactive dependency visualisation ships alongside.
The project publishes its own benchmark numbers: on SWE-bench Verified, correctness moving from 54% to 66%, with 42% token savings and 46% fewer tool calls, and up to 4× cheaper and 3× faster execution against its baseline. LLM provider support is vendor-neutral. Distributed on npm as @nanonets/graft under MIT, and actively released — the package has shipped continuously since July 2026.
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