GraphCanon updated 3w · GitHub synced 3w
Decision brief
A specialized command-line interface (CLI) tool focused on running Large Language Model (LLM) prompts and establishing a MCP (Model Context Protocol) client.
Good fit when
- - When you need a specific platform to interact with MCP clients directly from the command line, offering a streamlined option for integrating LLMs.
- - If your project involves using Model Context Protocol specifically, mcp-client-cli provides direct and easy access without requiring deep integration.
Avoid when
- - Avoid if you prefer a more generic CLI tool that supports a wide range of frameworks beyond MCP; mcp-client-cli is tailored toward MCP client interactions.
- - Not suitable for environments where the focus is on non-LLM tasks, as its functionality is centered around prompting and executing LLMs with MCP support.
- Requirements:
- Requires Python environment to run; specific dependencies may be needed as per the tool's configuration.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (237d since push)
- As of 3w
- Provenance
- Not a fork · Personal account
- As of 3w
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install mcp-client-cli PyPISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository contains a command-line interface (CLI) tool written in Python for interacting with language models using prompts and implementing the Model Context Protocol (MCP). It leverages frameworks like LangChain for its operations.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 27, 2026
- Languages
- python
Source: github.language+pyproject.toml · Jul 27, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Jul 27, 2026)
"base_url": "https://api.openai.com/v1" // Optional, for OpenRouter or other providersSource link
Source: README excerpt (regex_v1, Jul 27, 2026)
pip install mcp-client-cliSource link
Source: README excerpt (regex_v1, Jul 27, 2026)
Brave Search MCP Server running on stdioSource link
Source: README excerpt (regex_v1, Jul 27, 2026)
This act as alternative client beside Claude Desktop. Additionally you can use any LLM provider like OpenAI, Groq, or local LLM modeSource link
Tags
README
MCP CLI client
A simple CLI program to run LLM prompt and implement Model Context Protocol (MCP) client.
You can use any MCP-compatible servers from the convenience of your terminal.
This act as alternative client beside Claude Desktop. Additionally you can use any LLM provider like OpenAI, Groq, or local LLM model via llama.
Setup
-
Install via pip:
pip install mcp-client-cli -
Create a
~/.llm/config.jsonfile to configure your LLM and MCP servers:{ "systemPrompt": "You are an AI assistant helping a software engineer...", "llm": { "provider": "openai", "model": "gpt-4", "api_key": "your-openai-api-key", "temperature": 0.7, "base_url": "https://api.openai.com/v1" // Optional, for OpenRouter or other providers }, "mcpServers": { "fetch": { "command": "uvx", "args": ["mcp-server-fetch"], "requires_confirmation": ["fetch"], "enabled": true, // Optional, defaults to true "exclude_tools": [] // Optional, list of tool names to exclude }, "brave-search": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-brave-search"], "env": { "BRAVE_API_KEY": "your-brave-api-key" }, "requires_confirmation": ["brave_web_search"] }, "youtube": { "command": "uvx", "args": ["--from", "git+https://github.com/adhikasp/mcp-youtube", "mcp-youtube"] } } }Note:
- See CONFIG.md for complete documentation of the configuration format
- Use
requires_confirmationto specify which tools need user confirmation before execution - The LLM API key can also be set via environment variables
LLM_API_KEYorOPENAI_API_KEY - The config file can be placed in either
~/.llm/config.jsonor$PWD/.llm/config.json - You can comment the JSON config file with
//if you like to switch around the configuration
-
Run the CLI:
llm "What is the capital city of North Sumatra?"
Usage
Basic Usage
$ llm What is the capital city of North Sumatra?
The capital city of North Sumatra is Medan.
You can omit the quotes, but be careful with bash special characters like &, |, ; that might be interpreted by your shell.
You can also pipe input from other commands or files:
$ echo "What is the capital city of North Sumatra?" | llm
The capital city of North Sumatra is Medan.
$ echo "Given a location, tell me its capital city." > instructions.txt
$ cat instruction.txt | llm "West Java"
The capital city of West Java is Bandung.
Image Input
You can pipe image files to analyze them with multimodal LLMs:
$ cat image.jpg | llm "What do you see in this image?"
[LLM will analyze and describe the image]
$ cat screenshot.png | llm "Is there any error in this screenshot?"
[LLM will analyze the screenshot and point out any errors]
Using Prompt Templates
You can use predefined prompt templates by using the p prefix followed by the template name and its arguments:
# List available prompt templates
$ llm --list-prompts
# Use a template
$ llm p review # Review git changes
$ llm p commit # Generate commit message
$ llm p yt url=https://youtube.com/... # Summarize YouTube video
Triggering a tool
$ llm What is the top article on hackernews today?
================================== Ai Message ==================================
Tool Calls:
brave_web_search (call_eXmFQizLUp8TKBgPtgFo71et)
Call ID: call_eXmFQizLUp8TKBgPtgFo71et
Args:
query: site:news.ycombinator.com
count: 1
Brave Search MCP Server running on stdio
# If the tool requires confirmation, you'll be prompted:
Confirm tool call? [y/n]: y
=====================
For agents
This page has a .md twin and JSON over the API.