Frequently Asked Questions (FAQ) about Testmcp Python API

Q: What is the Testmcp Python API library?

A: The Testmcp Python API library provides a convenient way to access the Testmcp REST API from Python applications. It includes type definitions for request params and response fields and offers both synchronous and asynchronous clients.

Q: What is MCP (Model Context Protocol)?

A: MCP is an open protocol that standardizes how applications provide context to Large Language Models (LLMs), enabling AI models to access and interact with external data sources and tools.

Q: What are the key features of the Testmcp Python API library?

A: Key features include a Pythonic interface, type safety, asynchronous support, comprehensive error handling, retries and timeouts, logging, access to raw response data, streaming support, and a customizable HTTP client.

Q: How do I install the Testmcp Python API library?

A: You can install it from PyPI using pip: pip install --pre testmcpapi

Q: How do I use the library in synchronous mode?

A: Import Testmcp and create a client instance, providing your API key. Then, use the client to call API methods, such as client.product.list().

Q: How do I use the library in asynchronous mode?

A: Import AsyncTestmcp instead of Testmcp and use await with each API call. You’ll need to define an asynchronous function and use asyncio.run() to execute it.

Q: How do I handle errors with the library?

A: The library raises exceptions for various error conditions, such as APIConnectionError, RateLimitError, and APIStatusError. You can catch these exceptions and handle them accordingly.

Q: How do I configure retries and timeouts?

A: You can configure retries using the max_retries option when creating the client. Timeouts can be configured using the timeout option, which accepts a float or an httpx.Timeout object.

Q: How do I access raw response data, such as headers?

A: Use the .with_raw_response prefix to any HTTP method call, e.g., client.product.with_raw_response.list(). This returns an APIResponse object with access to the raw response.

Q: How do I stream responses?

A: Use .with_streaming_response and a context manager to stream the response body. You can then use methods like .iter_lines() to process the data.

Q: Can I customize the HTTP client?

A: Yes, you can override the httpx client to customize it for your use case, including support for proxies and custom transports.

Q: What is UBOS and how does it relate to Testmcp?

A: UBOS is a full-stack AI Agent Development Platform that complements the Testmcp library. UBOS provides tools for agent orchestration, data integration, custom agent development, and multi-agent systems, enhancing the development process.

Q: Where can I find more documentation and examples?

A: Refer to the praxis-cloud.com for REST API documentation and api.md for the full API of the library.