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. |
Huginn is presented as a robust, open-source alternative to IFTTT, offering greater customization, privacy through self-hosting, and the ability to handle complex workflows with API integrations. While it requires more technical expertise than IFTTT, it provides significantly more power and control.
This article explores the Model Context Protocol (MCP), an open protocol designed to standardize AI interaction with tools and data, addressing the fragmentation in AI agent ecosystems. It details current use cases, future possibilities, and challenges in adopting MCP.
GitHub Models now allows developers to retrieve structured JSON responses from models directly in the UI, improving integration with applications and workflows. Supported models include OpenAI (except for o1-mini and o1-preview) and Mistral models.
Hugging Face introduces a unified tool use API across multiple model families, making it easier to implement tool use in language models.
Hugging Face has extended chat templates to support tools, offering a unified approach to tool use with the following features:
- Defining tools: Tools can be defined using JSON schema or Python functions with clear names, accurate type hints, and complete docstrings.
- Adding tool calls to the chat: Tool calls are added as a field of assistant messages, including the tool type, name, and arguments.
- Adding tool responses to the chat: Tool responses are added as tool messages containing the tool name and content.