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.
The Universal Tool Calling Protocol (UTCP) is an open standard that describes how to call existing tools directly, eliminating the need for wrappers. It focuses on direct communication with tool endpoints (HTTP, gRPC, WebSocket, CLI, etc.) to reduce latency and maintain existing security and billing systems.
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. |
This guide highlights 10 open-source MCP (Model Context Protocol) servers to boost productivity in Cursor, covering tools for API work, web scraping, design integration, and document conversion.
| **Server Name** | **Primary Function** | **Key Features** |
|---|---|---|
| Apidog MCP Server | API Development | Syncs with API docs, natural language queries, local caching. |
| Browserbase MCP Server | Web Interaction & Automation | Cloud browser sessions, screenshots, JavaScript execution. |
| Magic MCP Server | Generative AI | Placeholder images, text transformation, code generation. |
| Opik MCP Server | Real-time Web Search | Web search integration, content summarization, source citations. |
| Figma Context MCP Server | Design-to-Code | Access Figma data, convert designs to code, analyze UI elements. |
| Pandoc MCP Server | Document Conversion | Converts between Markdown, PDF, HTML, DOCX, etc. |
| Excel MCP Server | Excel Data Access | Read/write Excel data, generate visualizations, automate reporting. |
| Mindmap MCP Server | Mindmap Integration | Import/interpret mindmaps, convert to outlines, collaborative planning. |
| Markdownify MCP Server | Content to Markdown | Converts HTML to Markdown, cleans documentation. |
| Tavily MCP Server | Curated Knowledge | High-quality knowledge retrieval, AI-friendly summaries, multi-source aggregation. |
| **Server Name** | **Primary Function** | **Key Features** |
|---|---|---|
| Apidog MCP Server | API Development | Syncs with API docs, natural language queries, local caching. |
| Browserbase MCP Server | Web Interaction & Automation | Cloud browser sessions, screenshots, JavaScript execution. |
| Magic MCP Server | Generative AI | Placeholder images, text transformation, code generation. |
| Opik MCP Server | Real-time Web Search | Web search integration, content summarization, source citations. |
| Figma Context MCP Server | Design-to-Code | Access Figma data, convert designs to code, analyze UI elements. |
| Pandoc MCP Server | Document Conversion | Converts between Markdown, PDF, HTML, DOCX, etc. |
| Excel MCP Server | Excel Data Access | Read/write Excel data, generate visualizations, automate reporting. |
| Mindmap MCP Server | Mindmap Integration | Import/interpret mindmaps, convert to outlines, collaborative planning. |
| Markdownify MCP Server | Content to Markdown | Converts HTML to Markdown, cleans documentation. |
| Tavily MCP Server | Curated Knowledge | High-quality knowledge retrieval, AI-friendly summaries, multi-source aggregation. |
This tutorial details how to use FastAPI-MCP to convert a FastAPI endpoint (fetching US National Park alerts) into an MCP-compatible server. It covers environment setup, app creation, testing, and MCP server implementation with Cursor IDE.
This article details a comparison between Model Context Protocol (MCP) and Function Calling, two methods for integrating Large Language Models (LLMs) with external systems. It covers their architectures, security models, scalability, and suitable use cases, highlighting the strengths and weaknesses of each approach.
MCP is best suited for robust, complex applications within secure enterprise environments, while Function Calling excels in straightforward, dynamic task execution scenarios. The choice depends on the specific needs, security requirements, scalability needs, and resource availability of the project.
MCP is best suited for robust, complex applications within secure enterprise environments, while Function Calling excels in straightforward, dynamic task execution scenarios. The choice depends on the specific needs, security requirements, scalability needs, and resource availability of the project.
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.
Model Context Protocol (MCP) is a bridging technology for AI agents and APIs. It standardizes API access for AI agents, making it a universal method for AI agents to trigger external actions.
MCP is an open-source standard that enhances interaction between AI systems and various data sources, improving usability, response quality, and security.
