This article lists and ranks the top Model Context Protocol (MCP) servers on GitHub as of June 2025, highlighting their capabilities and emphasizing the importance of security when granting agents access to sensitive data. It positions Pomerium as a solution for enforcing policy and securing agentic access to MCP servers.
|**GitHub Repository** |**Description** |
|---------------------------------|-----------------------------------------------------------------------------|
| github/github-mcp-server | Manages GitHub issues, pull requests, discussions with identity & permissions. |
| microsoft/playwright-mcp | Triggers browser automation tasks (QA, scraping, testing). |
| awslabs/mcp | Exposes AWS documentation, billing data, and service metadata. |
| hashicorp/terraform-mcp-server | Secure access to Terraform providers and modules. |
| dbt-labs/dbt-mcp | Exposes dbt’s semantic layer and CLI commands. |
| getsentry/sentry-mcp | Access to Sentry error tracking and performance telemetry. |
| mongodb-js/mongodb-mcp-server | Interacts with MongoDB and Atlas instances securely. |
| StarRocks/mcp-server-starrocks | Brings MCP to the StarRocks SQL engine. |
| vantage-sh/vantage-mcp-server |Focuses on cloud cost visibility. |
|**GitHub Repository** |**Description** |
|---------------------------------|-----------------------------------------------------------------------------|
| github/github-mcp-server | Manages GitHub issues, pull requests, discussions with identity & permissions. |
| microsoft/playwright-mcp | Triggers browser automation tasks (QA, scraping, testing). |
| awslabs/mcp | Exposes AWS documentation, billing data, and service metadata. |
| hashicorp/terraform-mcp-server | Secure access to Terraform providers and modules. |
| dbt-labs/dbt-mcp | Exposes dbt’s semantic layer and CLI commands. |
| getsentry/sentry-mcp | Access to Sentry error tracking and performance telemetry. |
| mongodb-js/mongodb-mcp-server | Interacts with MongoDB and Atlas instances securely. |
| StarRocks/mcp-server-starrocks | Brings MCP to the StarRocks SQL engine. |
| vantage-sh/vantage-mcp-server |Focuses on cloud cost visibility. |
China is experiencing a surge in AI agent development, sparked by Manus. While focusing initially on global markets due to China's internet restrictions, tech giants like ByteDance and Tencent are preparing to integrate AI agents into their super-apps. The article details the rise of Manus, Genspark, and Flowith, and the potential for China to lead in AI agent technology.
Kagent is an open-source agentic AI framework for Kubernetes that aims to provide autonomous problem solving and remediation for cloud-native infrastructure, moving beyond traditional automation to a more intelligent and self-healing system.
LLM 0.26 introduces tool support, allowing LLMs to access and utilize Python functions as tools. The article details how to install, configure, and use these tools with various LLMs like OpenAI, Anthropic, Gemini, and Ollama models, including examples with plugins and ad-hoc functions. It also discusses the implications for building 'agents' and future development plans.
The article details five security vulnerabilities in the Model Context Protocol (MCP): Tool Poisoning, Rug-Pull Updates, Retrieval-Agent Deception (RADE), Server Spoofing, and Cross-Server Shadowing. It explains how these vulnerabilities could compromise user safety and data integrity in AI agent systems.
Google today announced that the SDK for its Gemini models will natively support the Model Context Protocol from Anthropic. This move aims to simplify the connection between AI agents and data sources, aligning with the growing popularity of MCP and complementing Google's own Agent2Agent protocol. The company also plans to ease deployment of MCP servers and hosted tools for AI agents.
This course provides an introduction to the Model Context Protocol (MCP), covering its theory, design, and practical application. It includes foundational units, hands-on exercises, use case assignments, and collaboration opportunities. The course aims to equip students with the knowledge and skills to build AI applications leveraging external data and tools using MCP standards.
"A fully autonomous, AI-powered DevOps platform for managing cloud infrastructure across multiple providers, with AWS and GitHub integration, powered by OpenAI's Agents SDK."
This article details the creation of a simple, 50-line agent using Model Context Protocol (MCP) and Hugging Face's tools, demonstrating how easily agents can be built with modern LLMs that support function/tool calling.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
1. **MCP Overview**: MCP is a standard API for exposing tools that can be integrated with Large Language Models (LLMs).
2. **Implementation**: The author explains how to implement a MCP client using TypeScript and the Hugging Face Inference Client. This client connects to MCP servers, retrieves tools, and integrates them into LLM inference.
3. **Tools**: Tools are defined with a name, description, and parameters, and are passed to the LLM for function calling.
4. **Agent Design**: An agent is essentially a while loop that alternates between tool calling and feeding tool results back into the LLM until a specific condition is met, such as two consecutive non-tool messages.
5. **Code Example**: The article provides a concise 50-line TypeScript implementation of an agent, demonstrating the simplicity and power of MCP.
6. **Future Directions**: The author suggests experimenting with different models and inference providers, as well as integrating local LLMs using frameworks like llama.cpp or LM Studio.
Bethere.ai: The author introduces bethere.ai, a platform built to natively support both flows and state machines for building hybrid conversational experiences.
