Awesome-MCP and awesome-mcp

These are ecosystem siblings—both are independently maintained curated registries that serve the same aggregation function for the MCP ecosystem, with the first being more established (136 vs 10 stars) but overlapping in scope and purpose.

Awesome-MCP
53
Established
awesome-mcp
41
Emerging
Maintenance 13/25
Adoption 10/25
Maturity 9/25
Community 21/25
Maintenance 13/25
Adoption 5/25
Maturity 9/25
Community 14/25
Stars: 136
Forks: 35
Downloads: —
Commits (30d): 0
Language: —
License: CC0-1.0
Stars: 10
Forks: 3
Downloads: —
Commits (30d): 0
Language: JavaScript
License: —
No Package No Dependents
No Package No Dependents

About Awesome-MCP

AlexMili/Awesome-MCP

Awesome ModelContextProtocol resources - A curated list of MCP resources

The repository indexes both official Anthropic MCP servers and community implementations across multiple languages (TypeScript, Python, and others), spanning diverse integrations from databases and cloud platforms to browser automation and specialized tools. It organizes resources into servers, clients, SDKs, and tooling, enabling developers to discover MCP implementations for external data sources—from file systems and Git repositories to APIs like Slack, GitHub, and AWS. This comprehensive ecosystem index facilitates building LLM applications with protocol-standardized access to third-party services and data.

About awesome-mcp

gauravfs-14/awesome-mcp

A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Model Context Protocol (MCP) — a novel paradigm designed to enable modular, adaptive coordination between large language models (LLMs) and external tools or data contexts.

# Technical Summary Organizes 74+ peer-reviewed papers and implementations spanning MCP security frameworks, tool-orchestration architectures, and domain-specific applications (healthcare, IoT, wireless networks). Highlights emerging patterns in multi-agent LLM systems using structured tool routing, telemetry integration, and adaptive reasoning loops—from LangGraph-based designs to vision system extensions and distributed intelligence protocols. Covers critical concerns including OAuth-enhanced tool definitions, zero-trust registry approaches, and vulnerability audits for production MCP deployments across enterprise and autonomous systems.

Related comparisons

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Scores updated daily from GitHub, PyPI, and npm data. How scores work