Scrooge presentation image

Scrooge

Pre-edit situational awareness for AI coding agents.

Before an agent touches a file, Scrooge tells it the full scope of the change: what the file structurally calls, and — crucially — which other files have historically been edited together with it in the same commits, even without a direct code relationship.

This prevents the most common agent failure mode: incomplete edits — fixing the right file but missing the coupled module that also needed to change.

Available as a CLI tool and MCP server (plug directly into Claude Code and other agents).

The Problem

~35% of AI agent coding failures are incomplete edits (SWE-bench, 2024): the agent fixed the right location but missed a coupled module. Call-graph tools like Aider's repo map can't catch this — they only see structural connections (A calls B), not behavioral ones (A and B are always edited together).

How Scrooge Addresses It

Scrooge combines two signals:

1. Structural call graph — which functions call which, ranked by graph distance + PageRank. Finds the files directly involved in a query.

2. Co-change graph — mined from git log. Finds files that have historically been modified in the same commits, even with no direct code relationship. These are the implicit dependencies: shared invariants, parallel implementations, configuration that moves with logic.

Given a query or a file the agent is about to edit, Scrooge returns:

• Candidate files to read (structural)
• Co-change alerts: files that will almost certainly also need editing (behavioral)

Benchmark Results

Co-change: incomplete edit prevention

Tested on real git history from two Python libraries (50+ test cases total). Ground truth: commits that changed multiple source files simultaneously. Task: given one file, does the tool surface the others that were also edited?

Scrooge combined vs. Aider-style structural: +204% Recall@5, +95% MRR.

In 56% of real multi-file edits, Scrooge correctly surfaces all required co-changed files in the top 5. Structural-only navigation achieves 18%.

File navigation: finding the right module

Tested on 16 ground-truth queries across three Python repos (small/medium/large).

Full methodology: benchmarks/BENCHMARK_REPORT.md and benchmarks/COCHANGE_REPORT.md

How It Works

repo
 └── Scanner        → find all .py files
      └── Parser    → extract functions, classes, calls (Python AST)
           └── Graph Builder  → directed call graph (NetworkX)
                └── Ranker    → score nodes by query relevance
                     │          (graph distance + normalized PageRank + token coverage)
                     └── CLI / MCP  → return 2–3 ranked files with call context

Query flow

Scrooge matches queries by substring against code identifiers — function names, class names, method names, file names. For best results, pass symbol-oriented keywords, not natural language:

User: "how does the authentication flow work?"
         ↓
   Agent extracts keywords (built into MCP description)
         ↓
Scrooge query: "auth login authenticate"
         ↓
   Matches: auth.py → authenticate(), login_user()
         ↓
Returns: auth.py (relevance 100) + utils.py (relevance 72)
         with calls/called_by for each

This keyword extraction step is baked into the MCP tool descriptions, so agents using Claude Code do it automatically.

Output example

{
  "candidates": [
    {
      "file": "/path/to/auth.py",
      "relevance": 100,
      "matches": [{"symbol": "login_user", "line": 42}, {"symbol": "authenticate", "line": 61}],
      "calls": ["utils.normalize_username", "db.get_user"],
      "called_by": ["api.login_endpoint"]
    }
  ]
}

The agent reads auth.py starting at line 42. It knows before opening the file that it calls utils and db, and that the API layer calls into it.

Installation

Prerequisites: Python 3.11+, uv (recommended) or pip

git clone https://github.com/De-Cri/Scrooge.git
cd Scrooge
uv pip install -e .

Both scrooge (CLI) and scrooge-mcp (MCP server) are installed.

Setup as MCP Server (Claude Code)

Open your Claude Code settings file:

Add the Scrooge block inside mcpServers:

With uv (recommended):

{
  "mcpServers": {
    "Scrooge": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/Scrooge", "scrooge-mcp"]
    }
  }
}

With venv (Windows):

{
  "mcpServers": {
    "Scrooge": {
      "command": "C:/path/to/Scrooge/.venv/Scripts/python.exe",
      "args": ["-m", "mcp_server.scrooge_mcp"],
      "cwd": "C:/path/to/Scrooge"
    }
  }
}

Restart Claude Code. The architecture, connections, and index tools appear automatically. Claude will use them when exploring codebases — you don't need to prompt it differently.

CLI Usage

architecture — find files relevant to a query

scrooge architecture path/to/repo auth login

{
  "candidates": [
    {
      "file": "auth.py",
      "relevance": 100,
      "matches": [{"symbol": "login_user", "line": 42}],
      "calls": ["utils.normalize_username"],
      "called_by": ["api.login_endpoint"]
    }
  ]
}

Options:

• --rank-keep-pct (default 0.3) — fraction of top-ranked graph nodes to keep
• --file-keep-pct (default 0.35) — fraction of top-ranked files to keep

connections — trace call paths around matched symbols

scrooge connections path/to/repo auth login 2
scrooge connections path/to/repo auth login 2 --compact

MCP Tools

Project Structure

Scrooge/
├── scanner/scanner.py               # find source files
├── parser/ast_parser.py             # Python AST → functions, classes, calls
├── indexer/symbol_extractor.py      # match query tokens to symbol names + token coverage scoring
├── graph_builder/
│   ├── call_graph.py                # build NetworkX directed call graph
│   └── symbols_connections.py       # BFS traversal + connection output
├── intelligence/rank_graph_connections.py  # node ranking (normalized PageRank + distance)
├── cli/scrooge_cli.py               # CLI entry point
├── mcp_server/scrooge_mcp.py        # MCP server entry point
└── benchmarks/
    ├── objective_benchmark.py        # reproducible benchmark harness
    └── BENCHMARK_REPORT.md          # full results and analysis

Current Limitations

• Python only — AST parsing is implemented for Python. JS/TS file discovery exists but parsing is not implemented yet.
• No caching — every query re-parses the repo. Fast enough today (0.3–0.8s), but would need a cache for very large repos or high query frequency.
• Keyword queries only — Scrooge matches identifiers, not semantics. For vague conceptual questions, a semantic embedding search is complementary.
• Call resolution is best-effort — dynamic dispatch, decorators, and functools.partial are invisible to static AST analysis.

License

MIT