mcp-management

mrgoonie/claudekit-skills · updated Apr 8, 2026

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$npx skills add https://github.com/mrgoonie/claudekit-skills --skill mcp-management
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summary

Skill for managing and interacting with Model Context Protocol (MCP) servers.

skill.md

MCP Management

Skill for managing and interacting with Model Context Protocol (MCP) servers.

Overview

MCP is an open protocol enabling AI agents to connect to external tools and data sources. This skill provides scripts and utilities to discover, analyze, and execute MCP capabilities from configured servers without polluting the main context window.

Key Benefits:

  • Progressive disclosure of MCP capabilities (load only what's needed)
  • Intelligent tool/prompt/resource selection based on task requirements
  • Multi-server management from single config file
  • Context-efficient: subagents handle MCP discovery and execution
  • Persistent tool catalog: automatically saves discovered tools to JSON for fast reference

When to Use This Skill

Use this skill when:

  1. Discovering MCP Capabilities: Need to list available tools/prompts/resources from configured servers
  2. Task-Based Tool Selection: Analyzing which MCP tools are relevant for a specific task
  3. Executing MCP Tools: Calling MCP tools programmatically with proper parameter handling
  4. MCP Integration: Building or debugging MCP client implementations
  5. Context Management: Avoiding context pollution by delegating MCP operations to subagents

Core Capabilities

1. Configuration Management

MCP servers configured in .claude/.mcp.json.

Gemini CLI Integration (recommended): Create symlink to .gemini/settings.json:

mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json

See references/configuration.md and references/gemini-cli-integration.md.

2. Capability Discovery

npx tsx scripts/cli.ts list-tools  # Saves to assets/tools.json
npx tsx scripts/cli.ts list-prompts
npx tsx scripts/cli.ts list-resources

Aggregates capabilities from multiple servers with server identification.

3. Intelligent Tool Analysis

LLM analyzes assets/tools.json directly - better than keyword matching algorithms.

4. Tool Execution

Primary: Gemini CLI (if available)

gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"

Secondary: Direct Scripts

npx tsx scripts/cli.ts call-tool memory create_entities '{"entities":[...]}'

Fallback: mcp-manager Subagent

See references/gemini-cli-integration.md for complete examples.

Implementation Patterns

Pattern 1: Gemini CLI Auto-Execution (Primary)

Use Gemini CLI for automatic tool discovery and execution. See references/gemini-cli-integration.md for complete guide.

Quick Example:

gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"

Benefits: Automatic tool discovery, natural language execution, faster than subagent orchestration.

Pattern 2: Subagent-Based Execution (Fallback)

Use mcp-manager agent when Gemini CLI unavailable. Subagent discovers tools, selects relevant ones, executes tasks, reports back.

Benefit: Main context stays clean, only relevant tool definitions loaded when needed.

Pattern 3: LLM-Driven Tool Selection

LLM reads assets/tools.json, intelligently selects relevant tools using context understanding, synonyms, and intent recognition.

Pattern 4: Multi-Server Orchestration

Coordinate tools across multiple servers. Each tool knows its source server for proper routing.

Scripts Reference

scripts/mcp-client.ts

Core MCP client manager class. Handles:

  • Config loading from .claude/.mcp.json
  • Connecting to multiple MCP servers
  • Listing tools/prompts/resources across all servers
  • Executing tools with proper error handling
  • Connection lifecycle management

scripts/cli.ts

Command-line interface for MCP operations. Commands:

  • list-tools - Display all tools and save to assets/tools.json
  • list-prompts - Display all prompts
  • list-resources - Display all resources
  • call-tool <server> <tool> <json> - Execute a tool

Note: list-tools persists complete tool catalog to assets/tools.json with full schemas for fast reference, offline browsing, and version control.

Quick Start

Method 1: Gemini CLI (recommended)

npm install -g gemini-cli
mkdir -p .gemini && ln -sf .claude/.mcp.json .gemini/settings.json
gemini -y -m gemini-2.5-flash -p "Take a screenshot of https://example.com"

Method 2: Scripts

cd .claude/skills/mcp-management/scripts && npm install
npx tsx cli.ts list-tools  # Saves to assets/tools.json
npx tsx cli.ts call-tool memory create_entities '{"entities":[...]}'

Method 3: mcp-manager Subagent

See references/gemini-cli-integration.md for complete guide.

Technical Details

See references/mcp-protocol.md for:

  • JSON-RPC protocol details
  • Message types and formats
  • Error codes and handling
  • Transport mechanisms (stdio, HTTP+SSE)
  • Best practices

Integration Strategy

Execution Priority

  1. Gemini CLI (Primary): Fast, automatic, intelligent tool selection

    • Check: command -v gemini
    • Execute: gemini -y -m gemini-2.5-flash -p "<task>"
    • Best for: All tasks when available
  2. Direct CLI Scripts (Secondary): Manual tool specification

    • Use when: Need specific tool/server control
    • Execute: npx tsx scripts/cli.ts call-tool <server> <tool> <args>
  3. mcp-manager Subagent (Fallback): Context-efficient delegation

    • Use when: Gemini unavailable or failed
    • Keeps main context clean

Integration with Agents

The mcp-manager agent uses this skill to:

  • Check Gemini CLI availability first
  • Execute via gemini command if available
  • Fallback to direct script execution
  • Discover MCP capabilities without loading into main context
  • Report results back to main agent

This keeps main agent context clean and enables efficient MCP integration.

how to use mcp-management

How to use mcp-management on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add mcp-management
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/mrgoonie/claudekit-skills --skill mcp-management

The skills CLI fetches mcp-management from GitHub repository mrgoonie/claudekit-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/mcp-management

Reload or restart Cursor to activate mcp-management. Access the skill through slash commands (e.g., /mcp-management) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.625 reviews
  • Hassan Farah· Dec 24, 2024

    mcp-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Zaid Desai· Dec 20, 2024

    Useful defaults in mcp-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Dec 16, 2024

    I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Carlos Park· Nov 15, 2024

    mcp-management reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Maya Abbas· Nov 11, 2024

    I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· Nov 7, 2024

    Useful defaults in mcp-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ganesh Mohane· Oct 26, 2024

    mcp-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Advait Yang· Oct 6, 2024

    I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Valentina Sethi· Oct 2, 2024

    mcp-management reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Carlos Chen· Jul 27, 2024

    Registry listing for mcp-management matched our evaluation — installs cleanly and behaves as described in the markdown.

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