Skill for managing and interacting with Model Context Protocol (MCP) servers.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmcp-managementExecute the skills CLI command in your project's root directory to begin installation:
Fetches mcp-management from mrgoonie/claudekit-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate mcp-management. Access via /mcp-management in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Skill for managing and interacting with Model Context Protocol (MCP) servers.
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:
Use this skill when:
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.
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.
LLM analyzes assets/tools.json directly - better than keyword matching algorithms.
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.
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.
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.
LLM reads assets/tools.json, intelligently selects relevant tools using context understanding, synonyms, and intent recognition.
Coordinate tools across multiple servers. Each tool knows its source server for proper routing.
Core MCP client manager class. Handles:
.claude/.mcp.jsonCommand-line interface for MCP operations. Commands:
list-tools - Display all tools and save to assets/tools.jsonlist-prompts - Display all promptslist-resources - Display all resourcescall-tool <server> <tool> <json> - Execute a toolNote: list-tools persists complete tool catalog to assets/tools.json with full schemas for fast reference, offline browsing, and version control.
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.
See references/mcp-protocol.md for:
Gemini CLI (Primary): Fast, automatic, intelligent tool selection
command -v geminigemini -y -m gemini-2.5-flash -p "<task>"Direct CLI Scripts (Secondary): Manual tool specification
npx tsx scripts/cli.ts call-tool <server> <tool> <args>mcp-manager Subagent (Fallback): Context-efficient delegation
The mcp-manager agent uses this skill to:
gemini command if availableThis keeps main agent context clean and enables efficient MCP integration.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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mcp-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in mcp-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
mcp-management reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in mcp-management — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
mcp-management is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend mcp-management for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
mcp-management reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for mcp-management matched our evaluation — installs cleanly and behaves as described in the markdown.
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