Local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters — 6.8× fewer tokens on reviews and up to 49× on daily coding tasks.
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Code Review Graph is an MCP server that local knowledge graph for Claude Code. Builds a persistent map of your codebase so Claude reads only what matters — 6.8× fewer tokens on reviews and up to 49× on daily coding tasks. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 80/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"code-review-graph": {
"args": [
"serve",
"--tools",
"query_graph_tool,semantic_search_nodes_tool,detect_changes_tool,get_review_context_tool"
],
"command": "code-review-graph"
}
}
}Are you the author?
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AI coding tools re-read your entire codebase on every task. code-review-graph fixes that. It builds a structural map of your code with Tree-sitter, tracks changes incrementally, and gives your AI assistant precise context via MCP so it reads only what matters.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked code-review-graph against OSV.dev.
Click any tool to inspect its schema.
code_graph_databaseSQLite database storing the parsed code graph with nodes (functions, classes, imports) and edges (calls, inheritance, test coverage)
.code-review-graph/graph.db
build_code_review_graph_promptInstructs the AI assistant to build the code review graph for the project
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AI coding tools can end up re-reading large parts of your codebase on review tasks. code-review-graph fixes that. It builds a structural map of your code with Tree-sitter, tracks changes incrementally, and gives your AI assistant precise context via MCP so it reads only what matters.
pip install code-review-graph # or: pipx install code-review-graph
code-review-graph install # auto-detects and configures all supported platforms
code-review-graph build # parse your codebase
One command sets up everything. install detects which AI coding tools you have, writes the correct MCP configuration for each one, installs platform-native hooks/skills where supported, and injects graph-aware instructions into your platform rules. It auto-detects whether you installed via uvx or pip/pipx and generates the right config. Restart your editor/tool after installing.