agentic-mcp — for Claude Code agentic-mcp, Agentic-MCP-Skill, community, for Claude Code, ide skills, progressive MCP client, three-layer lazy loading, efficient token usage, AgentSkills.io pattern, MCP client tools, Claude Code

v1.0.0
GitHub

About this Skill

Perfect for AI Agents needing efficient token usage and optimized AI coding workflows with MCP servers. agentic-mcp is a progressive MCP client with three-layer lazy loading, designed for efficient token usage and optimized AI coding workflows.

Features

Start daemon using agentic-mcp daemon start
Get server info using agentic-mcp metadata
List available tools using agentic-mcp list
Get tool schema using agentic-mcp schema
Execute tools using agentic-mcp call
Manage daemon sessions using MCP_DAEMON_SESSION

# Core Topics

for Claude Code progressive MCP client three-layer lazy loading efficient token usage AgentSkills.io pattern MCP client tools
cablate cablate
[0]
[0]
Updated: 3/31/2026

Agent Capability Analysis

The agentic-mcp skill by cablate is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance. Optimized for for Claude Code, progressive MCP client, three-layer lazy loading.

Ideal Agent Persona

Perfect for AI Agents needing efficient token usage and optimized AI coding workflows with MCP servers.

Core Value

Empowers agents to validate AgentSkills.io patterns, manage MCP daemon sessions, and execute tools with JSON-based parameters, leveraging protocols like TCP and domain sockets, and supporting configuration files like 'mcp-servers.json'.

↓ Capabilities Granted for agentic-mcp

Automating MCP server metadata retrieval
Generating tool schemas for optimized AI coding
Debugging MCP daemon sessions with health checks and reload capabilities

! Prerequisites & Limits

  • Requires 'mcp-servers.json' configuration file
  • Dependent on MCP server availability and tool schema compatibility
  • JSON object parameters mandatory for tool calls via '--params'
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

agentic-mcp

Install agentic-mcp, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

Agentic MCP

Quick start

bash
1agentic-mcp daemon start --config <mcp-servers.json path>  # Start daemon
2agentic-mcp metadata <server-name>                  # Get server info
3agentic-mcp list <server-name>                      # List tools
4agentic-mcp schema <server-name> <tool-name>        # Get tool schema
5agentic-mcp call <server-name> <tool-name> --params '{"arg":"value"}'  # Call tool
6agentic-mcp daemon stop                             # Stop daemon

Core workflow

  1. agentic-mcp daemon start - Start daemon
  2. agentic-mcp metadata <server> - Layer 1: server info
  3. agentic-mcp list <server> - Layer 2: available tools
  4. agentic-mcp schema <server> <tool> - Layer 3: tool details
  5. agentic-mcp call <server> <tool> --params '{"arg":"value"}' - Execute tool

Daemon management

bash
1agentic-mcp daemon start                           # Start daemon
2agentic-mcp daemon health                          # Check status
3agentic-mcp daemon reload                          # Reload config
4agentic-mcp daemon stop                            # Stop daemon
5agentic-mcp daemon start --config <path>           # Custom config

Sessions (isolated instances):

bash
1MCP_DAEMON_SESSION=<name> agentic-mcp daemon start

Query commands

bash
1agentic-mcp metadata <server>                      # Server info
2agentic-mcp list <server>                          # List tools
3agentic-mcp schema <server> <tool>                 # Tool schema
4agentic-mcp daemon health                          # Check daemon status

Tool calls

bash
1agentic-mcp call <server> <tool> --params '{"argName":"value"}'

All parameters must be JSON object via --params.

JSON mode:

bash
1agentic-mcp metadata <server> --json
2agentic-mcp call <server> <tool> --params '{"arg":"value"}' --json

Configuration

mcp-servers.json:

json
1{
2  "servers": {
3    "<server-name>": {
4      "command": "npx",
5      "args": ["-y", "@scope/mcp-server"]
6    }
7  }
8}

Socket protocol

Command (newline-delimited JSON):

json
1{"id":"1","action":"metadata","server":"<server>"}

Response:

json
1{"id":"1","success":true,"data":{...}}

Platform: Windows (TCP) / Unix (domain socket)

Examples

Basic usage:

bash
1agentic-mcp daemon start
2agentic-mcp metadata <server>
3agentic-mcp list <server>
4agentic-mcp schema <server> <tool>
5agentic-mcp call <server> <tool> --params '{"arg":"value"}'

Multiple sessions:

bash
1MCP_DAEMON_SESSION=proj1 agentic-mcp daemon start
2MCP_DAEMON_SESSION=proj2 agentic-mcp daemon start

Debugging

bash
1agentic-mcp daemon health                          # Check status
2agentic-mcp daemon reload                          # Reload after config change

Errors

✗ MCP daemon is not running
✗ Server '<name>' not found
✗ Tool '<name>' not found
✗ Required argument '<name>' not provided

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is agentic-mcp?

Perfect for AI Agents needing efficient token usage and optimized AI coding workflows with MCP servers. agentic-mcp is a progressive MCP client with three-layer lazy loading, designed for efficient token usage and optimized AI coding workflows.

How do I install agentic-mcp?

Run the command: npx killer-skills add cablate/Agentic-MCP-Skill/agentic-mcp. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for agentic-mcp?

Key use cases include: Automating MCP server metadata retrieval, Generating tool schemas for optimized AI coding, Debugging MCP daemon sessions with health checks and reload capabilities.

Which IDEs are compatible with agentic-mcp?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for agentic-mcp?

Requires 'mcp-servers.json' configuration file. Dependent on MCP server availability and tool schema compatibility. JSON object parameters mandatory for tool calls via '--params'.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add cablate/Agentic-MCP-Skill/agentic-mcp. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use agentic-mcp immediately in the current project.

Related Skills

Looking for an alternative to agentic-mcp or another community skill for your workflow? Explore these related open-source skills.

View All

widget-generator

Logo of f
f

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

★ 149.6k
⑂ 0
AI

flags

Logo of vercel
vercel

flags is a Next.js feature management skill that enables developers to efficiently add or modify framework feature flags, streamlining React application development.

★ 138.4k
⑂ 0
Browser

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

★ 72.8k
⑂ 0
AI

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

★ 72.8k
⑂ 0
AI