What is Bin used for?

Bin is a lightweight testing service specifically designed for Model Context Protocol (MCP) environments. It helps developers simulate various client-server interactions to thoroughly test their MCP client implementations.

How does Bin assist with Large Language Model (LLM) testing?

Bin offers robust LLM sampling tests, supporting system prompts, custom parameters like temperature and max_tokens, and multi-turn conversation simulations, enabling deep evaluation of LLM integrations.

What types of interactions can Bin simulate for testing?

Bin can simulate a wide range of scenarios, including echoing input data, returning request context info, generating specific errors or random failures, and introducing delays to test client resilience and behavior.

Does Bin provide data generation capabilities for testing?

Yes, Bin can generate diverse data types such as UUIDs, current timestamps, and perform Base64 encoding/decoding. It also offers mock resources like random bytes and sample JSON for data fetching tests.

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Bin

Name: Bin
Author: bugffet

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Provides a lightweight testing service for Model Context Protocol (MCP) environments, offering a suite of endpoints for simulating various client-server interactions.

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`mcpbin` serves as a dedicated testing server for environments leveraging the Model Context Protocol (MCP), analogous to how httpbin aids in HTTP testing. Built with FastMCP, it equips developers with a comprehensive set of tools, resources, and prompts to thoroughly validate MCP client implementations. From basic data echo and context inspection to sophisticated error simulation, timing control, data generation, and even advanced LLM sampling and input elicitation, `mcpbin` offers a controlled environment to rigorously test how clients handle diverse protocol interactions and data flows.

Características Principales

Generate various data types such as UUIDs, timestamps, and base64 encoded/decoded strings.
Support different input elicitation types including strings, numbers, booleans, choices, and structured data.
0 GitHub stars
Simulate diverse MCP client-server interactions including echo, context retrieval, errors, and delays.
Facilitate comprehensive LLM sampling tests with system prompts, parameters, and multi-turn conversations.
Offer mock resources for testing data fetching, such as random bytes and sample JSON.

Casos de Uso

Rigorously test the robustness and correctness of MCP client implementations.
Aid in local development and debugging of MCP-enabled applications.
Validate how MCP clients handle various protocol states, errors, and data formats.

Acerca de

Características Principales

Generate various data types such as UUIDs, timestamps, and base64 encoded/decoded strings.
Support different input elicitation types including strings, numbers, booleans, choices, and structured data.
0 GitHub stars
Simulate diverse MCP client-server interactions including echo, context retrieval, errors, and delays.
Facilitate comprehensive LLM sampling tests with system prompts, parameters, and multi-turn conversations.
Offer mock resources for testing data fetching, such as random bytes and sample JSON.

Casos de Uso

Rigorously test the robustness and correctness of MCP client implementations.
Aid in local development and debugging of MCP-enabled applications.
Validate how MCP clients handle various protocol states, errors, and data formats.