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Suitable Loop

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Python

Suitable Loop

Local production engineering platform — semantic code analysis, git risk scoring, and log correlation via MCP.

What it does

Suitable Loop indexes your Python codebase and exposes deep analysis through MCP tools that AI assistants can call:

  • Code Analysis — AST-based parsing, function/class extraction, call graph construction, cyclomatic complexity

  • Git Risk Scoring — Weighted risk scores per commit (complexity delta × blast radius × churn × size)

  • Log Correlation — Parse logs, group errors by signature, map stack frames to indexed code

  • Dependency Graph — NetworkX-powered call graphs, import trees, blast radius analysis

Related MCP server: SocratiCode

Quick Start

1. Add MCP server to your project

Create .mcp.json in your project root:

{
  "mcpServers": {
    "suitable-loop": {
      "command": "uvx",
      "args": ["suitable-loop"]
    }
  }
}

That's it. uvx fetches the package from PyPI automatically — no manual install needed.

Local development? Point to a local checkout instead:

"args": ["--from", "/path/to/suitable-loop", "suitable-loop"]

2. Install slash commands (optional)

Clone the repo and copy the commands into your project:

git clone https://github.com/suitable-adventures/suitable-loop.git /tmp/suitable-loop
mkdir -p .claude/commands
cp /tmp/suitable-loop/.claude/commands/* .claude/commands/

3. Restart Claude Code and start using it

/onboard                          # Get a full codebase orientation
/risk-report                      # Analyze recent commits by risk
/impact-check models.py           # Check blast radius before changing a file
/debug-error "ConnectionResetError in db/pool.py"
/health-check                     # Full codebase health assessment
/trace-function my_function       # Map a function's callers and callees

Slash Commands

Command

What it does

/onboard

Index the codebase and produce a full orientation: structure, hotspots, complexity, blast radius

/risk-report

Score recent commits by risk, find hotspots, recommend where to focus review

/impact-check <file>

Before changing a file: blast radius, who calls what, what could break

/debug-error <error or log path>

Correlate an error/traceback to source code paths

/health-check

Full assessment: complexity, coupling, churn trends, actionable recommendations

/trace-function <name>

Map a function's callers, callees, complexity, and role in the system

/release [patch|minor|major]

Bump version, tag, and publish a new release to PyPI

MCP Tools

Code Analysis

Tool

Description

index_codebase

Index a Python project — parse AST, extract entities, build call graph

query_entity

Look up a function/class/file by name with all relationships

find_callers

Find all functions that call a given function

find_callees

Find all functions called by a given function

dependency_tree

Get import dependency tree for a file

search_code

Full-text search across indexed functions and classes

complexity_report

Top N most complex functions

codebase_summary

High-level stats and most-connected modules

Git Analysis

Tool

Description

analyze_recent_changes

Score recent commits by risk

analyze_commit

Deep-dive a single commit

hotspot_report

Files with high churn × high dependency count

blast_radius

Transitive impact of changing a file

Log Analysis

Tool

Description

ingest_logs

Parse log files, extract errors, group by signature

get_error_groups

List distinct error groups by frequency

error_detail

Full detail on an error group with code links

correlate_error

Map raw error text to code paths

error_timeline

Error frequency over time

Utility

Tool

Description

status

Server status and entity counts

reindex

Incremental re-index (only changed files)

Architecture

suitable_loop/
├── __init__.py          # Package root
├── __main__.py          # Entry point (python -m suitable_loop)
├── server.py            # MCP server setup (FastMCP) + main() entry point
├── config.py            # Configuration dataclasses
├── models.py            # Data models (entities, edges)
├── db.py                # SQLite database layer
├── analyzers/
│   ├── code_analyzer.py # AST parsing, call resolution
│   ├── git_analyzer.py  # Commit analysis, risk scoring
│   └── log_analyzer.py  # Log parsing, error grouping
├── graph/
│   └── engine.py        # NetworkX graph engine
└── tools/
    ├── code_tools.py    # MCP code analysis tools
    ├── git_tools.py     # MCP git analysis tools
    ├── log_tools.py     # MCP log analysis tools
    └── util_tools.py    # MCP utility tools

Configuration

Environment variables:

Variable

Default

Description

SUITABLE_LOOP_DB_PATH

~/.suitable-loop/suitable-loop.db

SQLite database path

SUITABLE_LOOP_LOG_LEVEL

INFO

Logging level

Default exclude patterns (files skipped during indexing):

  • .venv/, venv/, node_modules/, __pycache__/, migrations/, .git/

Development

For working on Suitable Loop itself:

cd suitable-loop
uv venv && uv pip install -e ".[dev]"
uv run pytest
Install Server
A
license - permissive license
B
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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