Using mcpc to Interact with MCP Servers
Use mcpc CLI to interact with MCP servers: call tools, read resources, and fetch prompts. Prefer key:=value bindings over raw JSON bodies for efficient command-line interaction.
name: using-mcp-tools-with-mcpc description: Use mcpc CLI to interact with MCP servers - call tools, read resources, get prompts. Use when working with Model Context Protocol servers, calling MCP tools, or accessing MCP resources programmatically; prefer key:=value bindings over raw JSON bodies. allowed-tools: Bash(mcpc:) Bash(node dist/cli/index.js:) Read Grep
mcpc: MCP command-line client
Use mcpc to interact with MCP (Model Context Protocol) servers from the command line.
This is more efficient than function calling - generate shell commands instead.
Trust pattern
- Always: Read-only mcpc commands in the sandbox (e.g.,
tools-list,tools-get,resources-list/read,prompts-list/get,tools-callfor read/search-only endpoints), session status checks, and commands that reuse already-created auth profiles. - Ask: Anything that writes or needs network/OAuth (login/logout must be human-initiated in the foreground), connecting to new servers, commands that create/update/delete data, helper scripts that write files, or when sandbox blocks the command.
- Never: Destructive workspace actions (moves/deletes) without explicit user request; connecting to unknown MCP servers without instruction; backgrounding
mcpc <server> loginor trying to auto-open a browser.
Quick reference
# List sessions and auth profiles
mcpc
# Show server info
mcpc <server>
mcpc @<session>
# Tools
mcpc <target> tools-list
mcpc <target> tools-get <tool-name>
mcpc <target> tools-call <tool-name> key:=value key2:="string value"
# Resources
mcpc <target> resources-list
mcpc <target> resources-read <uri>
# Prompts
mcpc <target> prompts-list
mcpc <target> prompts-get <prompt-name> arg1:=value1
# Sessions (persistent connections)
mcpc <server> connect @<name>
mcpc @<name> <command>
mcpc @<name> close
# Authentication
mcpc <server> login
mcpc <server> logout
Target types
mcp.example.com- Direct HTTPS connection to remote serverlocalhost:8080or127.0.0.1:8080- Local HTTP server (http:// is default for localhost)@session-name- Named persistent session (faster, maintains state)config-entry- Entry from config file (with--config)
Passing arguments
Prefer key:=value bindings. Use inline JSON only when needed (e.g., first-arg object or complex arrays):
# String values
mcpc @s tools-call search query:="hello world"
# Numbers, booleans, null (auto-parsed as JSON)
mcpc @s tools-call search query:="hello" limit:=10 enabled:=true
# Complex JSON values
mcpc @s tools-call search config:='{"nested":"value"}' items:='[1,2,3]'
# Force string type with JSON quotes
mcpc @s tools-call search id:='"123"'
# Inline JSON object (if first arg starts with { or [)
mcpc @s tools-call search '{"query":"hello","limit":10}'
# From stdin (auto-detected when piped)
echo '{"query":"hello"}' | mcpc @s tools-call search
JSON output for scripting
Always use --json flag for machine-readable output:
# Get tools as JSON
mcpc --json @apify tools-list
# Call tool and parse result with jq
mcpc --json @apify tools-call search query:="test" | jq '.content[0].text'
# Chain commands
mcpc --json @server1 tools-call get-data | mcpc @server2 tools-call process
Sessions for efficiency
Create sessions for repeated interactions:
# Create session (or reconnect if exists)
mcpc mcp.apify.com connect @apify
# Use session (faster - no reconnection overhead)
mcpc @apify tools-list
mcpc @apify tools-call search query:="test"
# Restart session (useful after server updates)
mcpc @apify restart
# Close when done
mcpc @apify close
Session states:
- 🟢 live - Bridge running, server might or might not be responding
- 🟡 crashed - Bridge crashed; auto-restarts on next use
- 🔴 expired - Server rejected session; needs
closeand reconnect
Authentication
OAuth (interactive login – human-only, foreground):
mcpc <server> loginopens the browser; mcpc never opens it itself. Do not background this command or it will miss the localhost callback.- Run login once per profile, then reuse the saved credentials in scripts.
- Re-run login to refresh/change scopes.
Python preflight to enforce “login first” in scripts (no automatic browser launches):
import json, os, sys
server = os.environ.get("MCP_SERVER", "mcp.apify.com")
profile = os.environ.get("MCP_PROFILE", "default")
profiles_path = os.path.join(os.path.expanduser("~"), ".mcpc", "profiles.json")
try:
data = json.load(open(profiles_path, "r", encoding="utf-8"))
profiles = data.get("profiles", [])
except FileNotFoundError:
profiles = []
has_profile = any(p.get("server") == server and p.get("name") == profile for p in profiles)
if not has_profile:
print(f"No mcpc auth profile '{profile}' for {server}.")
print(f"Run this yourself (foreground): mcpc {server} login --profile {profile}")
sys.exit(1)
After the preflight succeeds, scripts may call mcpc --profile <name> ... or rely on the default profile.
Bearer token:
mcpc -H "Authorization: Bearer $TOKEN" mcp.apify.com tools-list
mcpc -H "Authorization: Bearer $TOKEN" mcp.apify.com connect @myserver
Proxy server for AI isolation
Create a proxy MCP server that hides authentication tokens:
# Human creates authenticated session with proxy
mcpc mcp.apify.com connect @ai-proxy --proxy 8080
# AI agent connects to proxy (no access to original tokens)
# Note: localhost defaults to http://
mcpc localhost:8080 tools-list
mcpc 127.0.0.1:8080 connect @sandboxed
Common patterns
List and inspect tools:
mcpc @s tools-list
mcpc @s tools-get tool-name
Call tool and extract text result:
mcpc --json @s tools-call my-tool | jq -r '.content[0].text'
Read resource content:
mcpc @s resources-read "file:///path/to/file"
Use config file for local servers:
mcpc --config .vscode/mcp.json filesystem resources-list
Exit codes
0- Success1- Client error (invalid arguments)2- Server error (tool failed)3- Network error4- Authentication error
Debugging
# Verbose output shows protocol details
mcpc --verbose @s tools-call my-tool
Example script
See docs/examples/company-lookup.sh for a complete example
of an AI-generated script that validates prerequisites and calls MCP tools.
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