MarketNow Registry / AI/ML / hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298

hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298

this repo is for linkedin learning course: Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A)

AI/ML v0.0.0 by LinkedInLearning

Trust score

90/100

Install risk

Review advised

GitHub stars

Source

github

Indexed

2026-09-10

Install

$ git clone https://github.com/LinkedInLearning/hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298

Heuristic signals advise review before installing: young package, low adoption, stale repository, or name similarity to a popular package.

Adoption evidence: 41 GitHub stars.

How MarketNow scored this

Sentinel Index Heuristics — a security-first estimate from public signals:

Package age and release activity (stale repositories lose points)
Verified adoption: registry downloads or GitHub stars, not follower counts
Typosquat distance against the 150 most-installed MCP packages
Injection markers scanned in the package description

Heuristic, not a guarantee. For verified agent credentials and revocation, see the MarketNow Trust API.

What is hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298?

this repo is for linkedin learning course: Hands-On AI: Building AI Agents with Model Context Protocol (MCP) and Agent2Agent (A2A) Indexed by MarketNow with trust 90/100 (Review advised).

How do I install it?

Run $ git clone https://github.com/LinkedInLearning/hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298 Heuristic signals advise review before installing: young package, low adoption, stale repository, or name similarity to a popular package.

What does the trust score mean?

Security-first heuristic over public signals (age, adoption, typosquat distance, injection markers). It is not a popularity ranking and not a guarantee of safety. hands-on-ai-building-ai-agents-with-model-context-protocol-mcp-and-agent2agent-a2a-6055298 scores 90/100.