Anthropic is donating the Model Context Protocol (MCP) to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, to foster open and collaborative development of agentic AI.
Over the last year, MCP accomplished a rapid rise to popularity that few other standards or technologies have achieved so quickly. This article details the unlikely rise of the Model Context Protocol (MCP) and its journey to becoming a generally accepted standard for AI connectivity.
Tap these Model Context Protocol servers to supercharge your AI-assisted coding tools with powerful devops automation capabilities.
* **GitHub MCP Server:** Enables interaction with repositories, issues, pull requests, and CI/CD via GitHub Actions.
* **Notion MCP Server:** Allows AI access to notes and documentation within Notion workspaces.
* **Atlassian Remote MCP Server:** Connects AI tools with Jira and Confluence for project management and collaboration. (Currently in beta)
* **Argo CD MCP Server:** Facilitates interaction with Argo CD for GitOps workflows.
* **Grafana MCP Server:** Provides access to observability data from Grafana dashboards.
* **Terraform MCP Server:** Enables AI-driven Terraform configuration generation and management. (Local use only currently)
* **GitLab MCP Server:** Allows AI to gather project information and perform operations within GitLab. (Currently in beta, Premium/Ultimate customers only)
* **Snyk MCP Server:** Integrates security scanning into AI-assisted DevOps workflows.
* **AWS MCP Servers:** A range of servers for interacting with various AWS services.
* **Pulumi MCP Server:** Enables AI interaction with Pulumi organizations and infrastructure.
* **GitHub MCP Server:** Enables interaction with repositories, issues, pull requests, and CI/CD via GitHub Actions.
* **Notion MCP Server:** Allows AI access to notes and documentation within Notion workspaces.
* **Atlassian Remote MCP Server:** Connects AI tools with Jira and Confluence for project management and collaboration. (Currently in beta)
* **Argo CD MCP Server:** Facilitates interaction with Argo CD for GitOps workflows.
* **Grafana MCP Server:** Provides access to observability data from Grafana dashboards.
* **Terraform MCP Server:** Enables AI-driven Terraform configuration generation and management. (Local use only currently)
* **GitLab MCP Server:** Allows AI to gather project information and perform operations within GitLab. (Currently in beta, Premium/Ultimate customers only)
* **Snyk MCP Server:** Integrates security scanning into AI-assisted DevOps workflows.
* **AWS MCP Servers:** A range of servers for interacting with various AWS services.
* **Pulumi MCP Server:** Enables AI interaction with Pulumi organizations and infrastructure.
A comprehensive overview of the current state of Multi-Concept Prompting (MCP), including advancements, challenges, and future directions.
>The Playwright MCP Chrome Extension allows you to connect to pages in your existing browser and leverage the state of your default user profile. This means the AI assistant can interact with websites where you're already logged in, using your existing cookies, sessions, and browser state, providing a seamless experience without requiring separate authentication or setup.
This article compares Model Context Protocol (MCP), Function Calling, and OpenAPI Tools for integrating tools and resources with language models, outlining their strengths, limits, security considerations, and ideal use cases.
This article discusses the concept of 'tool masking' as a way to optimize the interaction between LLMs and APIs, arguing that simply exposing all API functionality (as done by MCP) is inefficient and degrades performance. It proposes shaping the tool surface to match the specific use case, improving accuracy, cost, and latency.
This article details how to use Playwright MCP and GitHub Copilot to reproduce and debug web app issues. It covers setup, a sample scenario, and the benefits of this debugging approach.
This article discusses Model Context Protocol (MCP), an open standard designed to connect AI agents with tools and data. It details the key components of MCP, its benefits (improved interoperability, future-proofing, and modularity), and its adoption in open-source agent frameworks like LangChain, CrewAI, and AutoGen. It also includes case studies of MCP implementation at Block and in developer tools.
MCP-Universe is a comprehensive benchmark designed to evaluate LLMs in realistic tasks through interaction with real-world MCP servers across 6 core domains and 231 tasks. It highlights the challenges of long-context reasoning, unfamiliar tool spaces, and cross-domain variations in LLM performance.
