Simplifying Api Consumption...

Since the Model Context Protocol (MCP) was announced by Anthropic a year ago, we’ve seen immense growth in large language models (LLMs) and agentic use cases. Before MCP became the de facto agentic standard, developers building agents on top of LLMs would have to hard-code the connective tissue between the LLM and apps. Developers would need to build custom integrations between their LLM client and the apps required by an end user’s prompt. With MCP, developers can now connect directly with external data sources, so their LLM can read data from and write data to the connected applications. But there’s a breaking point where things start to fall apart. The robustness and efficacy of agentic solutions depend on the quality of the application programming interfaces (APIs) that are used by an MCP server. MCPs expose tools that are invoked by LLMs, and these tools often reference individual API endpoints. The quality of APIs, therefore, directly correlates with the LLM client’s accurate discovery and execution of user prompts.