codegraph-nav 0.1.0

Graph-intelligent, token-efficient code navigation for AI assistants. Surgical reading, execution flows, risk scoring, and micro-metadata rendering.

codegraph-nav

Graph-intelligent, token-efficient code navigation for AI assistants

Surgical reading, execution flows, blast radius, risk scoring — 97% fewer tokens

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What is codegraph-nav?

codegraph-nav is an MCP server that helps AI assistants explore codebases efficiently. Beyond basic search and read, it adds graph intelligence: blast radius analysis, execution flow tracing, risk-scored diffs, and community detection.

┌─────────────────────────────────────────────────────────────┐
│                    WITHOUT CODEGRAPH-NAV                    │
├─────────────────────────────────────────────────────────────┤
│  User: "Fix the payment bug"                                │
│                                                             │
│  Claude reads:                                              │
│  • payments.py      (500 lines)  → 7,500 tokens             │
│  • billing.py       (300 lines)  → 4,500 tokens             │
│  • models/order.py  (200 lines)  → 3,000 tokens             │
│  ─────────────────────────────────────────────              │
│  Total:                            15,000 tokens            │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                     WITH CODEGRAPH-NAV                      │
├─────────────────────────────────────────────────────────────┤
│  User: "Fix the payment bug"                                │
│                                                             │
│  1. codegraph_get_minimal_context → 100 tokens              │
│  2. codegraph_search("payment")  → 100 tokens              │
│  3. codegraph_read(payments.py, 45, 89) → 400 tokens        │
│  4. codegraph_blast_radius → 100 tokens                     │
│  ─────────────────────────────────────────────              │
│  Total:                               700 tokens            │
│                                                             │
│  SAVINGS: 95% fewer tokens + impact analysis!               │
└─────────────────────────────────────────────────────────────┘

---

Quick Start

1. Install

pip install codegraph-nav[mcp]    # MCP server + core
pip install codegraph-nav[all]    # Everything (tree-sitter, networkx, ast-grep)

2. Configure Claude Code (CLI)

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "codegraph-nav": {
      "command": "codegraph-nav-mcp"
    }
  }
}

3. Configure Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "codegraph-nav": {
      "command": "codegraph-nav-mcp"
    }
  }
}

4. Use It

In Claude, just ask to explore your codebase:

"Scan this project and find the payment function"
"What's the blast radius if I change auth.py?"
"Show me the architecture of this codebase"

Claude will automatically use codegraph-nav's 19 tools to explore efficiently.

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Available Tools

Core Navigation

Tool	Purpose	When to Use
codegraph_get_minimal_context	~100 token project orientation	Always use first
codegraph_scan	Index codebase to .codegraph.json	First time on a project
codegraph_search	Find symbols by name/pattern	Looking for functions/classes
codegraph_read	Read specific line ranges	After finding symbol locations
codegraph_stats	Codebase size overview	Understanding project scale
codegraph_get_hubs	Find central files (PageRank)	Architecture analysis
codegraph_get_structure	File symbol outline	Before reading a file
codegraph_get_dependencies	Import/export graph	Understanding coupling
codegraph_test_gaps	Find untested symbols	Before/after changes

Graph Intelligence

Tool	Purpose	When to Use
codegraph_graph_build	Build graph DB (.codegraph.db)	Before heavy graph analysis
codegraph_blast_radius	Impact analysis (recursive CTE)	"What breaks if I change this?"
codegraph_list_flows	Execution flows + criticality	Understanding call chains
codegraph_detect_changes	Risk-scored git diff	Code review / PR analysis
codegraph_search_graph	FTS5 + fuzzy + RRF search	Search by concept, not just name

Architecture & Domain

Tool	Purpose	When to Use
codegraph_list_communities	Code communities + cohesion	Architecture grouping
codegraph_get_community	Community details + members	Explore a community
codegraph_get_architecture_overview	Architecture summary	High-level structure
codegraph_list_routes	HTTP routes (15+ frameworks)	API/route exploration
codegraph_list_schemas	ORM schemas (8+ ORMs)	Data model exploration

---

How It Works

flowchart TB
    subgraph SCAN["Step 1: Scan"]
        A[(Codebase)] --> B[codegraph_scan]
        B --> C[.codegraph.json]
    end

    subgraph NAVIGATE["Step 2: Navigate"]
        D[codegraph_search] --> E[file:line locations]
        E --> F[codegraph_read]
        F --> G[Only needed code]
    end

    subgraph GRAPH["Step 3: Analyze"]
        C --> H[codegraph_graph_build]
        H --> I[.codegraph.db]
        I --> J[blast_radius / flows / risk]
    end

    C --> D

1. Scan once - Creates .codegraph.json with all symbols indexed
2. Search & read surgically - Find symbols and load only the lines you need
3. Graph analysis - Build .codegraph.db for blast radius, flows, and risk scoring

---

Example Session

1. codegraph_get_minimal_context(path="/project", task="fix auth bug")
   → project: 142 files · 1847 symbols · py,js
   → hubs: config.py(8←), models.py(5←)
   → suggest: codegraph_search → codegraph_read → codegraph_get_dependencies

2. codegraph_search(query="authenticate")
   → src/auth.py:L10-30 [fn] authenticate

3. codegraph_read(file_path="src/auth.py", start_line=10, end_line=30)
   → Only those 20 lines (~200 tokens)

4. codegraph_blast_radius(path="/project", changed_files=["src/auth.py"])
   → blast(auth.py): 12 nodes · 5 files impacted

5. codegraph_detect_changes(path="/project")
   → risk: 0.78 HIGH | 3 files · 8 symbols changed

---

Detail Levels

All tools accept a detail_level parameter:

• minimal (default) - Compact, token-efficient output
• standard - Adds signatures, language breakdown, bidirectional counts
• verbose - Adds docstrings, dependency chains, per-file details

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Workflow Prompts

5 built-in prompts that enforce token discipline:

Prompt	Use Case
investigate_bug	Bug investigation: orient, search, read, dependencies
add_feature	Feature implementation: orient, search similar, structure, read
review_changes	Code review: orient, test gaps, search changed, read
understand_architecture	Architecture analysis: orient, hubs, dependencies, structure
onboard_project	Project onboarding: orient, stats, hubs, structure

Each prompt starts with codegraph_get_minimal_context and defaults to detail_level="minimal".

---

Supported Languages

Language	Analysis Type	Quality
Python	Full AST (stdlib)	⭐⭐⭐⭐⭐
JavaScript	AST (tree-sitter)*	⭐⭐⭐⭐⭐
TypeScript	AST (tree-sitter)*	⭐⭐⭐⭐⭐
Ruby	AST (tree-sitter)*	⭐⭐⭐⭐
Go	AST (tree-sitter)*	⭐⭐⭐⭐
Rust	AST (tree-sitter)*	⭐⭐⭐⭐
Dart / Flutter	AST (tree-sitter)**	⭐⭐⭐⭐
Java	Regex-based	⭐⭐⭐
C/C++	Regex-based	⭐⭐⭐
PHP	Regex-based	⭐⭐⭐

*Install tree-sitter support: pip install codegraph-nav[ast] All tree-sitter analyzers fall back to regex when tree-sitter is not installed.

**Dart/Flutter ships as its own extra: pip install codegraph-nav[dart]. The grammar (tree-sitter-dart) provides pre-compiled wheels for Linux/macOS/Windows — no C compiler needed. Without it, Dart falls back to the regex analyzer (classes, mixins, enums, extensions, top-level functions). Flutter needs no separate grammar; widgets are ordinary Dart classes.

---

Micro-Metadata Format

The scan output uses a compact ASCII tree with inline metadata:

my-project/
├── src/
│   ├── api/
│   │   ├── client.py [C:APIClient M:get,post] (5←)
│   │   └── routes.py [F:handle,validate] (3←)
│   └── core/
│       └── config.py [C:Config M:load] (Hub:8←)
└── tests/
    └── test_api.py [F:test_client]

═══ Summary ═══
28 files · 142 symbols · 12 hubs

Each file shows: classes (C:), functions (F:), methods (M:), and importer count (N←).

---

CLI Usage

# Generate code map
codegraph-nav scan /path/to/project
cgn scan .                              # Short alias

# Search for symbols
cgn search "process_payment"
cgn search -t class -o table

# Read specific lines
cgn read src/payments.py 45-89
cgn read src/payments.py 45-89 -c 3     # With context

# Get codebase stats
cgn stats

# Incremental update (only changed files)
cgn scan --incremental .

# Export as markdown
cgn export -f markdown -o docs/codebase.md

---

Python API

from codegraph_nav import CodeNavigator, CodeSearcher, LineReader

# Scan
navigator = CodeNavigator("/path/to/project")
code_map = navigator.scan()

# Search
searcher = CodeSearcher(".codegraph.json")
results = searcher.search_symbol("payment", symbol_type="function")

# Read surgically
reader = LineReader(root_path="/path/to/project")
content = reader.read_lines("src/pay.py", 45, 89)

Graph API

from codegraph_nav.graph import GraphStore, GraphBuilder, get_blast_radius, trace_flows

# Build graph from code map
store = GraphStore(".codegraph.db")
builder = GraphBuilder(store)
stats = builder.build_from_code_map(code_map)

# Blast radius
impact = get_blast_radius(store, changed_files=["src/auth.py"])
print(f"{impact['impacted_nodes']} nodes, {len(impact['impacted_files'])} files affected")

# Execution flows
flows = trace_flows(store)
for f in flows[:5]:
    print(f"{f['name']} crit:{f['criticality']:.2f} depth:{f['depth']}")

store.close()

Domain Intelligence API

from codegraph_nav.domain import detect_routes, detect_schemas, infer_project_tags

# Detect routes (FastAPI, Express, Django, Gin, etc.)
routes = detect_routes(code_map, root_path="/path/to/project")

# Detect ORM schemas (SQLAlchemy, Django, Prisma, etc.)
schemas = detect_schemas(code_map, root_path="/path/to/project")

# Infer domain tags (auth, db, cache, api, etc.)
tags = infer_project_tags(code_map)

---

Optional Dependencies

Extra	What it adds
[mcp]	MCP server (FastMCP)
[ast]	Tree-sitter analyzers (JS/TS/Ruby/Go/Rust)
[graph]	PageRank hub detection (networkx)
[fast]	ast-grep high-perf parsing
[all]	Everything above
[dev]	pytest, mypy, black, ruff, build, twine

---

Performance

Tested on real-world open source projects:

Project	Files	Symbols	Map Size	Map Time
Flask	142	1,847	89 KB	0.8s
Django	2,156	28,493	1.2 MB	8.2s
requests	47	412	23 KB	0.3s

Token savings:

• Small projects (50-100 files): 85-90% reduction
• Medium projects (100-500 files): 92-96% reduction
• Large projects (500+ files): 97-99% reduction

---

Development

git clone https://github.com/efrenbl/codegraph-nav.git
cd codegraph-nav
python3 -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
python3 -m pytest tests/ -v

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Requirements

• Python 3.10+
• MCP SDK 1.0+ (for MCP server only)

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License

MIT License - see LICENSE for details.

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Stop burning tokens reading entire files.
Scan once, search instantly, read surgically, analyze impact.