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mcpbin is a lightweight testing service similar to httpbin, designed specifically for MCP (Model Context Protocol) environments.
Configuration
View docs{
"mcpServers": {
"bugffet-mcpbin": {
"url": "https://mcpbin.fastmcp.app/mcp"
}
}
}You can use this MCP server to test MCP client implementations with a hosted endpoint and a local development route. It provides a ready-to-use HTTP endpoint for remote clients and a simple stdio flow for local testing, along with a suite of tools to exercise common MCP capabilities.
Connect your MCP client to the hosted endpoint to test remote MCP interactions. Use the HTTP URL provided for standard MCP communication, which supports typical MCP client workflows.
For local development and testing, run the built-in development command to start a local MCP server instance that you can connect to from your client. This lets you experiment with prompts, timing controls, and the tool suite in a controlled environment.
Prerequisites: ensure you have a shell environment with the ability to run build and development commands. You should have a toolchain available to execute the provided commands.
Step 1: Synchronize or install development tooling needed for MCP work. Run the following command in your terminal:
Step 2: Start the local development server to test MCP client interactions locally by running the development command:
Hosted MCP endpoint you can connect to from your client.
Start a local MCP development server to test interactions in isolation.
Echo back input data to verify request/response handling.
Return information about the request context to help you understand metadata and routing.
Raise an error with a message and code to test error handling paths.
Return either a success or an error to simulate intermittent failure.
Pause execution for a specified duration to test timing and timeouts.
Generate a UUID version 4 string for testing unique identifiers.
Return the current timestamp for timing-related tests.
Encode input data to Base64 to verify encoding/transport.
Decode Base64 data back to its original form.
Perform basic sampling on input data to exercise the sampling workflow.
Sampling that uses a predefined system prompt to shape outputs.
Sampling with temperature and max_tokens to explore output variation.
Handle multi-turn conversations to test context retention.
Request a string input from the client to simulate user prompts.
Request an integer input from the client to test numeric prompts.
Request a boolean choice from the client to validate decision prompts.
Request a selection from a list of options to test options handling.
Request structured data to validate complex data collection.