mcp-cli

github/awesome-copilot · updated Apr 8, 2026

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$npx skills add https://github.com/github/awesome-copilot --skill mcp-cli
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summary

Command-line interface for discovering and executing MCP server tools and external integrations.

  • Five core commands cover server discovery, tool exploration, schema inspection, execution, and grep-based searching across all available tools
  • Supports JSON input/output for scripting, raw text extraction, and description flags for verbose tool documentation
  • Handles complex JSON arguments via heredoc, stdin piping, or file input to accommodate special characters and multi-line payloads
skill.md

MCP-CLI

Access MCP servers through the command line. MCP enables interaction with external systems like GitHub, filesystems, databases, and APIs.

Commands

Command Output
mcp-cli List all servers and tool names
mcp-cli <server> Show tools with parameters
mcp-cli <server>/<tool> Get tool JSON schema
mcp-cli <server>/<tool> '<json>' Call tool with arguments
mcp-cli grep "<glob>" Search tools by name

Add -d to include descriptions (e.g., mcp-cli filesystem -d)

Workflow

  1. Discover: mcp-cli → see available servers and tools
  2. Explore: mcp-cli <server> → see tools with parameters
  3. Inspect: mcp-cli <server>/<tool> → get full JSON input schema
  4. Execute: mcp-cli <server>/<tool> '<json>' → run with arguments

Examples

# List all servers and tool names
mcp-cli

# See all tools with parameters
mcp-cli filesystem

# With descriptions (more verbose)
mcp-cli filesystem -d

# Get JSON schema for specific tool
mcp-cli filesystem/read_file

# Call the tool
mcp-cli filesystem/read_file '{"path": "./README.md"}'

# Search for tools
mcp-cli grep "*file*"

# JSON output for parsing
mcp-cli filesystem/read_file '{"path": "./README.md"}' --json

# Complex JSON with quotes (use heredoc or stdin)
mcp-cli server/tool <<EOF
{"content": "Text with 'quotes' inside"}
EOF

# Or pipe from a file/command
cat args.json | mcp-cli server/tool

# Find all TypeScript files and read the first one
mcp-cli filesystem/search_files '{"path": "src/", "pattern": "*.ts"}' --json | jq -r '.content[0].text' | head -1 | xargs -I {} sh -c 'mcp-cli filesystem/read_file "{\"path\": \"{}\"}"'

Options

Flag Purpose
-j, --json JSON output for scripting
-r, --raw Raw text content
-d Include descriptions

Exit Codes

  • 0: Success
  • 1: Client error (bad args, missing config)
  • 2: Server error (tool failed)
  • 3: Network error
how to use mcp-cli

How to use mcp-cli 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-cli
2

Execute installation command

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

$npx skills add https://github.com/github/awesome-copilot --skill mcp-cli

The skills CLI fetches mcp-cli from GitHub repository github/awesome-copilot 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-cli

Reload or restart Cursor to activate mcp-cli. Access the skill through slash commands (e.g., /mcp-cli) 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.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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)
  • No comments yet — start the thread.
general reviews

Ratings

4.635 reviews
  • Pratham Ware· Dec 16, 2024

    mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Xiao Malhotra· Dec 12, 2024

    Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.

  • Sakshi Patil· Nov 7, 2024

    Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.

  • Min Gill· Nov 3, 2024

    mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Chaitanya Patil· Oct 26, 2024

    We added mcp-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min Verma· Oct 22, 2024

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

  • Min Gupta· Sep 17, 2024

    Keeps context tight: mcp-cli is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Piyush G· Sep 5, 2024

    mcp-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Tariq Farah· Sep 1, 2024

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

  • Anika Mehta· Sep 1, 2024

    mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.

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