Use the mcp CLI tool to dynamically discover and invoke MCP server capabilities without pre-configuring them as permanent integrations.
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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 obra/superpowers-lab 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.
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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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Use the mcp CLI tool to dynamically discover and invoke MCP server capabilities without pre-configuring them as permanent integrations.
Use this skill when you need to:
The mcp CLI must be installed at ~/.local/bin/mcp. If not present:
# Clone and build
cd /tmp && git clone --depth 1 https://github.com/f/mcptools.git
cd mcptools && CGO_ENABLED=0 go build -o ~/.local/bin/mcp ./cmd/mcptools
Always ensure PATH includes the binary:
export PATH="$HOME/.local/bin:$PATH"
mcp tools <server-command>
Examples:
# Filesystem server
mcp tools npx -y @modelcontextprotocol/server-filesystem /path/to/allow
# Memory/knowledge graph server
mcp tools npx -y @modelcontextprotocol/server-memory
# GitHub server (requires token)
mcp tools docker run -i --rm -e GITHUB_PERSONAL_ACCESS_TOKEN ghcr.io/github/github-mcp-server
# HTTP-based server
mcp tools https://example.com/mcp
mcp resources <server-command>
Resources are data sources the server exposes (files, database entries, etc.).
mcp prompts <server-command>
Prompts are pre-defined prompt templates the server provides.
# For full schema details including parameter types
mcp tools --format json <server-command>
mcp tools --format pretty <server-command>
mcp call <tool_name> --params '<json>' <server-command>
Read a file:
mcp call read_file --params '{"path": "/tmp/example.txt"}' \
npx -y @modelcontextprotocol/server-filesystem /tmp
Write a file:
mcp call write_file --params '{"path": "/tmp/test.txt", "content": "Hello world"}' \
npx -y @modelcontextprotocol/server-filesystem /tmp
List directory:
mcp call list_directory --params '{"path": "/tmp"}' \
npx -y @modelcontextprotocol/server-filesystem /tmp
Create entities (memory server):
mcp call create_entities --params '{"entities": [{"name": "Project", "entityType": "Software", "observations": ["Uses TypeScript"]}]}' \
npx -y @modelcontextprotocol/server-memory
Search (memory server):
mcp call search_nodes --params '{"query": "TypeScript"}' \
npx -y @modelcontextprotocol/server-memory
For nested objects and arrays, ensure valid JSON:
mcp call edit_file --params '{
"path": "/tmp/file.txt",
"edits": [
{"oldText": "foo", "newText": "bar"},
{"oldText": "baz", "newText": "qux"}
]
}' npx -y @modelcontextprotocol/server-filesystem /tmp
# Table (default, human-readable)
mcp call <tool> --params '{}' <server>
# JSON (for parsing)
mcp call <tool> --params '{}' -f json <server>
# Pretty JSON (readable JSON)
mcp call <tool> --params '{}' -f pretty <server>
# List available resources
mcp resources <server-command>
# Read a specific resource
mcp read-resource <resource-uri> <server-command>
# Alternative syntax
mcp call resource:<resource-uri> <server-command>
# List available prompts
mcp prompts <server-command>
# Get a prompt (may require arguments)
mcp get-prompt <prompt-name> <server-command>
# With parameters
mcp get-prompt <prompt-name> --params '{"arg": "value"}' <server-command>
If using a server frequently during a session:
# Create alias
mcp alias add fs npx -y @modelcontextprotocol/server-filesystem /home/user
# Use alias
mcp tools fs
mcp call read_file --params '{"path": "README.md"}' fs
# List aliases
mcp alias list
# Remove when done
mcp alias remove fs
Aliases are stored in ~/.mcpt/aliases.json.
mcp tools --auth-user "username:password" https://api.example.com/mcp
mcp tools --auth-header "Bearer your-token-here" https://api.example.com/mcp
mcp tools docker run -i --rm \
-e GITHUB_PERSONAL_ACCESS_TOKEN="$GITHUB_TOKEN" \
ghcr.io/github/github-mcp-server
mcp tools npx -y @modelcontextprotocol/server-filesystem /tmp
mcp tools https://example.com/mcp
mcp tools http://localhost:3001/sse
# Or explicitly:
mcp tools --transport sse http://localhost:3001
# Allow access to specific directory
mcp tools npx -y @modelcontextprotocol/server-filesystem /path/to/allow
mcp tools npx -y @modelcontextprotocol/server-memory
export GITHUB_PERSONAL_ACCESS_TOKEN="your-token"
mcp tools docker run -i --rm -e GITHUB_PERSONAL_ACCESS_TOKEN ghcr.io/github/github-mcp-server
export BRAVE_API_KEY="your-key"
mcp tools npx -y @anthropic/mcp-server-brave-search
mcp tools npx -y @anthropic/mcp-server-puppeteer
Before calling tools, run mcp tools to understand what's available and the exact parameter schema.
When you need to process results programmatically:
mcp call <tool> --params '{}' -f json <server> | jq '.field'
The table output shows parameter signatures. Match them exactly:
param:str = stringparam:num = numberparam:bool = booleanparam:str[] = array of strings[param:str] = optional parameterTool calls may fail. Check exit codes and stderr:
if ! result=$(mcp call tool --params '{}' server 2>&1); then
echo "Error: $result"
fi
If making several calls to the same server:
mcp alias add tmp-server npx -y @modelcontextprotocol/server-filesystem /tmp
mcp call list_directory --params '{"path": "/tmp"}' tmp-server
mcp call read_file --params '{"path": "/tmp/file.txt"}' tmp-server
mcp alias remove tmp-server
For safety, limit what tools are accessible:
# Only allow read operations
mcp guard --allow 'tools:read_*,list_*' --deny 'tools:write_*,delete_*' \
npx -y @modelcontextprotocol/server-filesystem /home
mcp tools --server-logs <server-command>
cat ~/.mcpt/aliases.json
Use --format pretty for detailed JSON output to debug parameter issues.
| Action | Command |
|---|---|
| List tools | mcp tools <server> |
| List resources | mcp resources <server> |
| List prompts | mcp prompts <server> |
| Call tool | mcp call <tool> --params '<json>' <server> |
| Read resource | mcp read-resource <uri> <server> |
| Get prompt | mcp get-prompt <name> <server> |
| Add alias | mcp alias add <name> <server-command> |
| Remove alias | mcp alias remove <name> |
| JSON output | Add -f json or -f pretty |
# 1. Discover what's available
mcp tools npx -y @modelcontextprotocol/server-filesystem /home/user/project
# 2. Check for resources
mcp resources npx -y @modelcontextprotocol/server-filesystem /home/user/project
# 3. Create alias for convenience
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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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
mcp-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.
We added mcp-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for mcp-cli matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: mcp-cli is focused, and the summary matches what you get after install.
mcp-cli is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
mcp-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
mcp-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend mcp-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: mcp-cli is the kind of skill you can hand to a new teammate without a long onboarding doc.
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