Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs 

Large Language Models (LLMs) are increasingly evolving from passive text generators into active agents that can perform realworld tasks by invoking external tools. To support this shift, scalable protocols for tool integration are essential. The Model Context Protocol (MCP), introduced by Anthropic in late 2024, offers a schema-driven standard for dynamic tool discovery and invocation. Yet, building MCP servers remains manual and repetitive, requiring developers to write glue code, handle authentication, and configure schemas by hand, replicating much of the integration effort MCP aims to eliminate. This paper investigates whether MCP server construction can be meaningfully automated. We begin by analyzing adoption trends: from over 22,000 MCP-tagged GitHub repositories created within six months of release, fewer than 5% include servers, typically small, single-maintainer projects dominated by repetitive scaffolding. To address this gap, we present AutoMCP, a compiler that generates MCP servers from OpenAPI 2.0/3.0 specifications. AutoMCP parses REST API definitions and produces complete server implementations, including schema registration and authentication handling. We evaluate it on 50 real-world APIs spanning 5,066 endpoints and more than 10 domains. From a stratified sample of 1,023 tool calls, 76.5% succeeded out-of-the-box. Manual failure analysis revealed five recurring issues, all attributable to inconsistencies or omissions in the OpenAPI contracts. After minor fixes, averaging just 19 lines of spec changes per API, AutoMCP achieved 99.9% success. Our findings (i) analyze MCP adoption and quantify the cost of manual server development, (ii) demonstrate that OpenAPI specifications, despite quality issues, enable near-complete MCP server automation, and (iii) contribute a corpus of 5,066 callable tools along with insights on repairing common specification flaws. These results shift the adoption bottleneck from code generation to specification quality, offering a path toward LLM-native tool ecosystems. 

Example Agentic workflow that uses Model Context Protocol for LLM-tool integration