Is the cursor-github-agent-test MCP server actively maintained?

The cursor-github-agent-test MCP server was last committed 2d ago and has 1 contributor. Issue health: 50% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

cursor-github-agent-test MCP Server

Name: Junming93/cursor-github-agent-test
Rating: 3.0 (1 reviews)
Author: Junming93

Junming93/cursor-github-agent-test

Score: 49.6 Rank #86 MCP Server

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A repository exploring GitHub automation with Model Context Protocol (MCP)

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Overview

Junming93/cursor-github-agent-test is a MCP server. A repository exploring GitHub automation with Model Context Protocol (MCP)

Ranked #86 out of 109 indexed tools.

Actively maintained with commits in the last week.

Ecosystem

No license

Score Breakdown

Stars 15% 1

1 stars → early stage

Freshness 25% 2d ago

Last commit 2d ago → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 1

1 contributor → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 0

Description Good

License None

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

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Contributors medium impact

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Details

Stars: 1
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 1
Dependents: —
Language: —
License: —
Last Commit: 2d ago

Matched Queries

"model context protocol""model-context-protocol"

From the README

# GitHub Agent

A repository exploring GitHub automation using the **Model Context Protocol (MCP)**.

---

## What is Model Context Protocol (MCP)?

**Model Context Protocol (MCP)** is an open standard introduced by Anthropic in November 2024 that defines a universal, structured way for AI models (like large language models) to connect with external tools, data sources, and services. It acts as a "universal adapter" — similar to what USB-C is for hardware — enabling seamless, consistent integrations between AI assistants and the systems they interact with.

---

## Why MCP?

Before MCP, every AI tool integration required custom, one-off implementations. Developers had to build bespoke connectors for each combination of AI model and external service, leading to:

- **Fragmented ecosystems** — integrations that don't work across different AI providers
- **Duplicated effort** — rebuilding the same logic repeatedly
- **Inconsistent behavior** — each integration behaved differently

MCP sol

Read full README on GitHub →