Command-line interface for discovering and executing MCP server tools and external integrations.
Works with
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
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionmcp-cliExecute the skills CLI command in your project's root directory to begin installation:
Fetches mcp-cli from github/awesome-copilot 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-cli. Access via /mcp-cli 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.
Submit your Claude Code skill and start earning
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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Access MCP servers through the command line. MCP enables interaction with external systems like GitHub, filesystems, databases, and APIs.
| 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)
mcp-cli → see available servers and toolsmcp-cli <server> → see tools with parametersmcp-cli <server>/<tool> → get full JSON input schemamcp-cli <server>/<tool> '<json>' → run with arguments# 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\": \"{}\"}"'
| Flag | Purpose |
|---|---|
-j, --json |
JSON output for scripting |
-r, --raw |
Raw text content |
-d |
Include descriptions |
0: Success1: Client error (bad args, missing config)2: Server error (tool failed)3: Network errorMake 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.
github/awesome-copilot
github/awesome-copilot
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.
mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added mcp-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in mcp-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: mcp-cli is the kind of skill you can hand to a new teammate without a long onboarding doc.
mcp-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for mcp-cli matched our evaluation — installs cleanly and behaves as described in the markdown.
mcp-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.
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