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Psqasim
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mcp-tools

Build MCP servers and clients exposing tools, resources, and prompts via JSON-RPC.

1|1|Updated Feb 10, 2026
Search Tags:#automation#resources#mcp#prompts#tools#model-context-protocol
One-click install
npx skills add https://github.com/Psqasim/personal-ai-employee --skill mcp-tools-psqasim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-tools
Source: https://github.com/Psqasim/personal-ai-employee/tree/main/.claude/skills/mcp-tools
Command: npx skills add https://github.com/Psqasim/personal-ai-employee --skill mcp-tools-psqasim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

MCP enables AI agents to connect to external tools, data sources, and prompts using a unified protocol, eliminating ad-hoc integrations and enabling scalable automation.

Core Features & Use Cases

  • Exposes Tools, Resources, and Prompts via a standard API so AI apps can call functions, read data, and template prompts.
  • Supports building MCP servers and clients for automation, discovery, and orchestration across stdio and HTTP SSE transports.
  • Real-world use: compose multi-app workflows where an AI queries data, executes tools, and generates prompts for downstream tasks.

Quick Start

Start by connecting to a sample MCP server and performing a tool call, reading a resource, and retrieving a prompt template.

Frequently Asked Questions about mcp-tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the Model Context Protocol and how does it connect AI to external tools?▼

MCP uses JSON-RPC to let AI apps call functions, read data, and template prompts via standard interfaces. It supports modular templates, async patterns, and validation to enable safe, scalable deployments across stdio and HTTP SSE transports.

How do I build an MCP server for automation and tool discovery?▼

To build an MCP server, expose tools, resources, and prompts via JSON-RPC. Apply async patterns and validation to support orchestration and discovery across stdio and HTTP SSE transports for scalable automation workflows.

Can I use MCP to orchestrate multi-app workflows with AI agents?▼

MCP supports multi-app workflow orchestration by allowing AI agents to query data, execute tools, and generate prompts for downstream tasks. This requires connecting servers and clients across stdio or HTTP SSE transports.

Does the Model Context Protocol support both stdio and HTTP SSE transports?▼

MCP supports both stdio and HTTP SSE transports for building servers and clients. This flexibility enables safe, scalable automation and orchestration across various communication environments using JSON-RPC.

What are the limitations of using MCP for AI agent integration?▼

MCP requires careful architectural planning to expose resources, prompts, and tools via JSON-RPC. While it standardizes AI agent integrations, implementing async patterns, validation, and modular templates for scalable deployments across transports introduces complexity.

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