hawkeye-analyzer 0.1.2

Python dependency graph analyzer, architecture enforcer, and MCP server for AI coding agents

🦅 Hawkeye

Python architectural intelligence engine — dependency analysis, symbol-level impact tracking, and MCP server for AI coding agents.

Hawkeye gives AI editors (Claude Code, Cursor, Windsurf) full architectural awareness before they edit your code — preventing broken imports, hidden coupling, and circular dependencies.

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Installation

# From source (recommended during beta)
git clone https://github.com/AlexxBenny/Hawkeye-analyze-your-codebase.git
cd Hawkeye-analyze-your-codebase
pip install -e .

# With MCP server for AI editors
pip install -e ".[mcp]"

Quick Start

# 1. Analyze any Python project
hawkeye analyze /path/to/project

# 2. Get full architectural context for a file
hawkeye context /path/to/project src/core/engine.py

# 3. Check symbol-level impact before refactoring
hawkeye impact /path/to/project src/core/engine.py -s Engine

# 4. Find coupling hotspots
hawkeye impact /path/to/project src/core/engine.py --hotspots

# 5. Find dead code
hawkeye impact /path/to/project src/core/engine.py --unused

# 6. Open interactive dependency graph
hawkeye show /path/to/project

# 7. Enforce architecture rules in CI
hawkeye check /path/to/project --no-cycles

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Why Hawkeye?

Problem	How Hawkeye Solves It
"I changed a class and 5 tests broke"	hawkeye impact shows blast radius before you edit
"This codebase has spaghetti imports"	hawkeye analyze maps the entire dependency graph
"Which module is the riskiest?"	hawkeye metrics ranks by instability + complexity
"Are there circular imports?"	hawkeye check --no-cycles detects and suggests fixes
"AI editor made a bad import"	MCP server gives the AI full context before it writes

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CLI Commands (7)

Command	Description
hawkeye analyze <path>	Full project analysis (text/json/dot/html output)
hawkeye context <path> <file...>	Architectural context for one or more files
hawkeye impact <path> <file>	Symbol-level impact analysis, hotspots, dead code
hawkeye show <path>	Open interactive D3.js graph in browser
hawkeye check <path>	Enforce architecture rules (CI/CD exit codes)
hawkeye metrics <path>	Coupling + complexity metrics table
hawkeye serve	Start MCP server for AI editors

Example: hawkeye analyze

=== PROJECT SUMMARY: MyApp ===

  Modules:          20
  Dependencies:     40
  Total LOC:        2696
  Graph density:    0.1053
  Avg instability:  0.444
  Has cycles:       No ✅

  Health breakdown:
    ✅ Healthy:     9
    ⚠️  Warning:    5
    🔴 Critical:    6

── Module Metrics ──
Module                       Ca   Ce       I    LOC   Health
MyApp.core.graph              8    2   0.200    251     🔴
MyApp.core.metrics            5    1   0.167    144     ⚠️
MyApp.core.analyzer           3    1   0.250    328     🔴
MyApp.config                  4    0   0.000    102     ✅

Example: hawkeye impact --hotspots

🔥 Symbol Hotspots (imported by ≥2 modules):

  DependencyGraph       (core.graph)     → 16 importers
  ModuleMetrics         (core.metrics)   → 10 importers
  HawkeyeEngine         (engine)         →  6 importers

Example: hawkeye impact --unused

💀 Unused Symbols (5 found):

  EdgeInfo              (core.graph)        ← safe to make private
  analyze_file          (core.analyzer)     ← superseded by analyze_project

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AI Editor Integration (MCP)

Hawkeye exposes 10 MCP tools designed for AI coding agents. Add to your editor's MCP config:

{
  "mcpServers": {
    "hawkeye": {
      "command": "hawkeye-mcp",
      "args": ["--project", "/path/to/your/project"]
    }
  }
}

MCP Tools

Tool	Purpose
hawkeye_analyze(path)	Scan project — call this first
hawkeye_file_context(file)	Everything about a file in one call — deps, dependents, impact, cycles, health
hawkeye_context(files)	Combined context for multi-file editing sessions
hawkeye_impact(file, symbol, mode)	Symbol-level blast radius, hotspots, dead code
hawkeye_symbols(file)	List all classes/functions with usage counts
hawkeye_find(pattern)	Search for modules by name
hawkeye_cycles()	Import cycles with severity and break suggestions
hawkeye_metrics(sort_by)	Coupling + complexity metrics for all modules
hawkeye_path(source, target)	Shortest dependency path between modules
hawkeye_graph(max_depth)	Full graph as structured JSON

Example: hawkeye_file_context

One call gives the AI agent everything it needs:

{
  "module": "myapp.core.engine",
  "file": "core/engine.py",
  "loc": 340,
  "dependency_count": 3,
  "dependent_count": 8,
  "dependencies": [
    {"module": "myapp.core.scanner", "file": "core/scanner.py"},
    {"module": "myapp.core.analyzer", "file": "core/analyzer.py"}
  ],
  "dependents": [
    {"module": "myapp.cli", "file": "cli.py"},
    {"module": "myapp.server.mcp", "file": "server/mcp.py"}
  ],
  "impact": {"direct": 8, "transitive": 12},
  "metrics": {
    "ca": 8, "ce": 3, "instability": 0.273,
    "health": "critical",
    "cyclomatic_complexity": 45,
    "cognitive_complexity": 32
  }
}

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Architecture Rules

Define rules in hawkeye.toml and enforce them with hawkeye check:

[project]
name = "MyApp"

[scan]
exclude_dirs = ["venv", "__pycache__", ".git"]

# Enforce layered architecture (lower layers can't import higher ones)
[rules.layers]
order = ["models", "services", "api", "cli"]
direction = "downward"

# Block specific import patterns
[[rules.forbidden]]
from = "api.*"
to = ["cli.*", "scripts.*"]

# Ensure module groups are independent
[[rules.independence]]
modules = ["auth", "billing", "notifications"]

# CI/CD integration — exits non-zero on violations
hawkeye check /path/to/project --no-cycles

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Core Analysis Features

Dependency Graph

• AST-based import resolution (relative, absolute, symbol-level)
• Directed graph with transitive closure, shortest path, neighborhood extraction
• Topological sort, depth filtering, subgraph extraction

Coupling Metrics (Robert C. Martin)

• Ca — Afferent coupling (who depends on me)
• Ce — Efferent coupling (who do I depend on)
• I — Instability (Ce / (Ca + Ce)) — 0.0 = stable, 1.0 = unstable

Complexity Metrics

• Cyclomatic complexity — decision branches per function
• Cognitive complexity — nesting-weighted (SonarSource specification)
• Per-function, per-class, and per-module aggregation

Cycle Detection

• Tarjan's Strongly Connected Components (SCC) algorithm
• Severity scoring (low → critical based on cycle size + coupling)
• Break suggestions with specific edge recommendations

Symbol Resolution

• Cross-file symbol registry (class X defined in module Y)
• Import → definition matching
• Hotspot detection (most-imported symbols)
• Dead code detection (defined but never imported)
• Symbol-level impact analysis (blast radius per class/function)

Health Scoring

• Composite of coupling + complexity → healthy / warning / critical
• Per-module and project-level aggregation

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Visualization

Interactive HTML (D3.js)

hawkeye show /path/to/project           # Opens in browser
hawkeye analyze /path/to/project -f html -o graph.html

Force-directed graph with search, zoom, drag, click-to-inspect. Color-coded by health status.

Graphviz DOT

hawkeye analyze /path/to/project -f dot -o deps.dot
dot -Tpng deps.dot -o deps.png

JSON

hawkeye analyze /path/to/project -f json

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Project Structure

Hawkeye/
├── src/hawkeye/
│   ├── engine.py               # Central orchestrator (9-step pipeline)
│   ├── config.py               # TOML config with walk-up discovery
│   ├── cli.py                  # 7 CLI commands
│   ├── core/                   # Analysis pipeline
│   │   ├── scanner.py          #   File discovery + LOC counting
│   │   ├── analyzer.py         #   AST imports + symbols + complexity
│   │   ├── graph.py            #   Directed graph + algorithms
│   │   ├── metrics.py          #   Coupling + complexity metrics
│   │   ├── cycles.py           #   Tarjan's SCC + severity scoring
│   │   ├── rules.py            #   Architecture rule enforcement
│   │   └── symbols.py          #   Cross-file symbol resolution
│   ├── server/                 # AI editor integration
│   │   └── mcp.py              #   10 MCP tools
│   └── visualizer/             # Output renderers
│       ├── html_renderer.py    #   Interactive D3.js graph
│       ├── dot_renderer.py     #   Graphviz DOT format
│       ├── text_renderer.py    #   Terminal tables + summaries
│       └── json_renderer.py    #   Structured JSON
├── tests/                      # 195 tests (pytest)
│   ├── conftest.py             #   Shared fixtures
│   ├── test_scanner.py         #   17 tests
│   ├── test_analyzer.py        #   22 tests
│   ├── test_graph.py           #   16 tests
│   ├── test_metrics.py         #   15 tests
│   ├── test_cycles.py          #   14 tests
│   ├── test_rules.py           #   16 tests
│   ├── test_symbols.py         #   27 tests
│   └── test_engine.py          #   22 tests
├── examples/                   # Usage examples
├── hawkeye.toml                # Example config
├── pyproject.toml              # pip install ready
└── LICENSE                     # MIT

Zero required dependencies. Core analysis runs on Python stdlib only. MCP server requires pip install mcp.

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Requirements

• Python 3.10+
• No dependencies for core analysis
• mcp package for AI editor integration (optional)

License

MIT — see LICENSE for details.