Mcp Server Dig

B+ 85 completed
Api
cli / typescript · small
199
Files
28,540
LOC
1
Frameworks
5
Languages

Pipeline State

completed
Run ID
#363882
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
52.00
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #49413
Member of a group with 1 similar repo(s) — canonical #23729 view group →
Top concepts (2)
Project DescriptionTesting
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

Create a command-line tool, similar to an AI code archaeology service, that lets developers analyze a git repository. I need functionality to query git blame, view file history, and analyze contributor patterns. Specifically, I'd like tools to assess repository health, identify code ownership changes over time, and perform blast radius analysis for any given file or directory. Also, include the ability to compare repository metrics between two different git references, like tags or branches. The tool should be built using TypeScript.
typescript cli git code-archaeology analysis developer-tool repository command-line
Generated by gemma4:latest

Catalog Information

A server that delivers AI‑powered code archaeology services, enabling developers to query git blame, file history, contributor patterns, and change analysis.

Description

The server exposes a set of endpoints that return detailed git blame information, file revision histories, and contributor activity metrics. It leverages AI models to surface patterns in code changes, predict maintenance hotspots, and suggest refactoring opportunities. Developers can query specific lines, files, or time ranges to obtain actionable insights. The service is designed for teams that need deeper visibility into repository evolution and for maintainers who want to prioritize code quality improvements. By automating the extraction and interpretation of git data, it reduces manual effort and speeds up code review and audit processes.

الوصف

يُقدّم الخادم مجموعة من نقاط النهاية التي تُرجِع معلومات تفصيلية عن blame في Git، وتاريخ إصدارات الملفات، ومقاييس نشاط المساهمين. يستفيد من نماذج الذكاء الاصطناعي لتسليط الضوء على الأنماط في تغييرات الكود، وتوقع مناطق الصيانة الحرجة، وتقديم اقتراحات لإعادة هيكلة الكود. يمكن للمطورين استعلام عن سطور محددة، أو ملفات، أو فترات زمنية للحصول على رؤى عملية. صُمم للخدمات التي تحتاج إلى رؤية أعمق لتطور المستودعات وللمحافظ الذين يرغبون في أولوية تحسين جودة الكود. من خلال أتمتة استخراج وتفسير بيانات Git، يقلل من الجهد اليدوي ويسرّع عمليات مراجعة الكود والتدقيق.

Novelty

7/10

Tags

code-history-analysis git-blame contributor-insights change-impact-detection ai-driven-repository-analytics developer-productivity open-source-maintenance

Technologies

vitest zod

Claude Models

claude-opus-4.6

Quality Score

B+
85.0/100
Structure
88
Code Quality
89
Documentation
68
Testing
85
Practices
82
Security
100
Dependencies
60

Strengths

  • Well-documented README with substantial content
  • CI/CD pipeline configured (github_actions)
  • Good test coverage (80% test-to-source ratio)
  • Code linting configured (eslint)
  • Consistent naming conventions (kebab-case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • 639 duplicate lines detected \u2014 consider DRY refactoring

Security & Health

7.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot
MIT
License
12.3%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

typescript
75.8%
json
13.8%
markdown
9.5%
yaml
0.8%
javascript
0.1%

Frameworks

Vitest

Concepts (2)

Repobility · code-quality intelligence · https://repobility.com
CategoryNameDescriptionConfidence
Powered by Repobility — scan your code at https://repobility.com
auto_descriptionProject DescriptionMCP server for AI-powered code archaeology — git blame, file history, contributor patterns, and related change analysis80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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