To connect your Cline AI agent to a local MCP server, you'll need to configure it using the STDIO transport method, which is designed specifically for local servers running on your machine[1].
Cline stores all MCP server settings in a file called cline_mcp_settings.json[1]. This file uses a JSON format with an mcpServers object containing your server configurations.
A well-structured package-notes.md file should serve as a comprehensive reference for your package.json decisions, making it easy for team members (including your future self) to understand the rationale behind dependencies, scripts, and configuration choices.
Here's a proven structure that covers all the essential aspects of documenting your package.json:
Prompt PPLX.ai:
"what are some good ideas for MCP services to make. that doesnt already exist in the marketplace? Services where there is a growing need?"
Based on current market trends and emerging gaps in the managed cloud platform (MCP) services landscape, here are innovative service ideas that address growing needs:
With 98% of IT decision-makers reporting cloud skills gaps[1] and 75% of businesses struggling with talent shortages[2], there's a massive opportunity for:
TypeScript error handling involves several best practices that leverage the language's type system to create more robust and maintainable applications. Here are the key strategies and techniques:
The traditional try-catch block remains fundamental for error handling in TypeScript[2]. However, TypeScript enhances this with type safety considerations:
try {
// Code that might throw an error
} catch (error: unknown) {
When choosing the right talent assessment tool for your startup, it's crucial to balance scientific validity, practical application, cost-effectiveness, and team-building benefits. Here's a comprehensive comparison of five popular assessment tools and their suitability for startup environments.
Created by Ray Dalio in collaboration with psychologists, PrinciplesYou offers a comprehensive personality assessment based on the Big Five model with additional dimensions[1][2].
Key Features:
MCP: The Protocol That's Quietly Rewiring How AI Systems Connect
The pretext is simple: we're witnessing the second wave of AI
The first wave was AI wrappers—brilliant but isolated islands of intelligence. Companies like Perplexity, Cursor, and Replit built impressive tools that essentially put a beautiful interface on top of someone else's LLM[1][2][3]. These AI wrappers were initially dismissed as "just an API call with a prompt"[2], but they quietly became multi-billion dollar businesses while the tech world obsessed over foundation models.
Now we're entering something fundamentally different. **The second wave isn't
Your concerns about MCP's potential decline are well-founded and reflect real challenges the protocol faces. Let me analyze each point based on current industry trends and adoption patterns.
This concern has significant merit. The relationship between RAG and MCP is more complex than simple replacement, and enterprise preferences are already showing interesting patterns.
The three MCP (Model Context Protocol) observability solutions each offer distinct approaches to monitoring and observability, targeting different user needs and technical requirements.
SigNoz's primary differentiator is its commitment to open standards and vendor-neutral observability[1]. By leveraging OpenTelemetry (OTel), it ensures that organizations aren't locked into proprietary solutions and can maintain full ownership of their telemetry data[1].