Quickly test and explore MCP servers from the command line!
A simple, text-based CLI client for Model Context Protocol (MCP) servers built with LangChain and TypeScript.
Suitable for testing MCP servers, exploring their capabilities, and prototyping integrations.
- Node.js 18+
- [optional]
uv
(uvx
) installed to run Python-based local (stdio) MCP servers - API keys from Anthropic, OpenAI, and/or Google GenAI as needed
-
Install
mcp-try-cli
tool. This can take up to a few minutes to complete:npm install -g @h1deya/mcp-try-cli
-
Configure LLM and MCP Servers settings via the configuration file,
llm_mcp_config.json5
code llm_mcp_config.json5
The following is a simple configuration for quick testing:
{ "llm": { "model_provider": "openai", "model": "gpt-4o-mini", // "model_provider": "anthropic", // "model": "claude-3-5-haiku-latest", // "model_provider": "google_genai", // "model": "gemini-2.0-flash", }, "mcp_servers": { "weather": { "command": "npx", "args": ["-y", "@h1deya/mcp-server-weather"] }, }, "example_queries": [ "Tell me how LLMs work in a few sentences", "Are there any weather alerts in California?", ], }
-
Set up API keys
echo "ANTHROPIC_API_KEY=sk-ant-... OPENAI_API_KEY=sk-proj-... GOOGLE_API_KEY=AI..." > .env code .env
-
Run the tool
mcp-try-cli
By default, it reads the configuration file,
llm_mcp_config.json5
, from the current directory.
Then, it applies the environment variables specified in the.env
file, as well as the ones that are already defined.
See README_DEV.md
- Easy setup: Works out of the box with popular MCP servers
- Flexible configuration: JSON5 config with environment variable support
- Multiple LLM providers: OpenAI, Anthropic, Google Gemini
- Command & URL servers: Support for both local and remote MCP servers
- Real-time logging: Live stdio MCP server logs with customizable log directory
- Interactive testing: Example queries for the convenience of repeated testing
mcp-try-cli
By default, it reads the configuration file, llm_mcp_config.json5
, from the current directory.
Then, it applies the environment variables specified in the .env
file,
as well as the ones that are already defined.
It outputs local MCP server logs to the current directory.
# Specify the config file to use
mcp-try-cli --config my-config.json5
# Store local (stdio) MCP server logs in specific directory
mcp-try-cli --log-dir ./logs
# Enable verbose logging
mcp-try-cli --verbose
# Show help
mcp-try-cli --help
- OpenAI:
gpt-4o
,gpt-4o-mini
, etc. - Anthropic:
claude-sonnet-4-0
,claude-3-5-haiku-latest
, etc. - Google (GenAI):
gemini-2.0-flash
,gemini-1.5-pro
, etc.
Create a llm_mcp_config.json5
file:
- The configuration file format
for MCP servers follows the same structure as
Claude for Desktop,
with one difference: the key name
mcpServers
has been changed tomcp_servers
to follow the snake_case convention commonly used in JSON configuration files. - The file format is JSON5, where comments and trailing commas are allowed.
- The format is further extended to replace
${...}
notations with the values of corresponding environment variables. - Keep all the credentials and private info in the
.env
file and refer to them with${...}
notation as needed
{
"llm": {
"model_provider": "openai",
"model": "gpt-4o-mini",
// model: "o4-mini",
},
// "llm": {
// "model_provider": "anthropic",
// "model": "claude-3-5-haiku-latest",
// // "model": "claude-sonnet-4-0",
// },
// "llm": {
// "model_provider": "google_genai",
// "model": "gemini-2.0-flash",
// // "model": "gemini-2.5-pro-preview-06-05",
// }
"example_queries": [
"Tell me how LLMs work in a few sentences",
"Are there any weather alerts in California?",
"Read the news headlines on bbc.com",
],
"mcp_servers": {
// Local MCP server that uses `npx`
"weather": {
"command": "npx",
"args": [ "-y", "@h1deya/mcp-server-weather" ]
},
// Another local server that uses `uvx`
"fetch": {
"command": "uvx",
"args": [ "mcp-server-fetch" ]
},
"brave-search": {
"command": "npx",
"args": [ "-y", "@modelcontextprotocol/server-brave-search" ],
"env": { "BRAVE_API_KEY": "${BRAVE_API_KEY}" }
},
// Remote MCP server via URL
// Auto-detection: tries Streamable HTTP first, falls back to SSE
"remote-mcp-server": {
"url": "https://api.example.com/..."
},
// Server with authentication
"github": {
"type": "http", // recommended to specify the protocol explicitly when authentication is used
"url": "https://api.githubcopilot.com/mcp/",
"headers": {
"Authorization": "Bearer ${GITHUB_PERSONAL_ACCESS_TOKEN}"
}
}
}
}
Create a .env
file for API keys:
OPENAI_API_KEY=sk-ant-...
ANTHROPIC_API_KEY=sk-proj-...
GOOGLE_API_KEY=AI...
# Other services as needed
GITHUB_PERSONAL_ACCESS_TOKEN=github_pat_...
BRAVE_API_KEY=BSA...
There are quite a few useful MCP servers already available:
- Missing API key: Make sure your
.env
file contains the required API key - Server not found: Ensure MCP server packages are available via npx
- Permission errors: Check file permissions for log directory
- Check the logs in your specified log directory
- Use
--verbose
flag for detailed output - Refer to MCP documentation
This tool is built with:
- Model Context Protocol (MCP)
- LangChain for LLM integration
- TypeScript for type safety
- Yargs for CLI parsing
MIT License - see LICENSE file for details.
Issues and pull requests welcome! This tool aims to make MCP server testing as simple as possible.