Use MCP services on demand via CLI without preloading extra context.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "mcp-cli" skill from askskill: 1. Download https://raw.githubusercontent.com/obra/superpowers-lab/main/skills/mcp-cli/SKILL.md 2. Save it as ~/.claude/skills/mcp-cli/SKILL.md 3. Reload skills and tell me it's ready
Using mcp-cli, list the tools, resources, and prompts available from the accessible MCP servers and group them by use case.
A categorized inventory of available capabilities for quickly understanding what can be used.
I want to organize project documents automatically and retrieve version information. Use mcp-cli to find the most suitable MCP tools or resources and explain why.
A recommended list of MCP tools or resources, with use cases and selection rationale for each.
Use mcp-cli to temporarily invoke the needed MCP service for a task query and return only a concise result summary without loading unrelated integration details.
A lightweight, focused result summary that avoids introducing unnecessary context.
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
…
Audit codebases to find semantically duplicate functions with different names or implementations.
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Clarify intent, requirements, and solution direction before any creative implementation work.
Create isolated workspaces for feature development without disturbing current code.
Set conversation rules to discover and invoke skills before replying.
Turn requirements into a clear step-by-step execution plan before implementation.
Call tools like weather lookup via MCP with reusable resources and prompts.
Turn almost any CLI tool into an MCP service for AI assistants.
Demo MCP server for calculations, time checks, notes, and code review prompts.
Turn CLI tools or REST APIs into MCP servers for Claude.
Create and run custom multi-language tools dynamically for MCP clients.
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