A Framework for Testing and Adapting REST APIs as LLM Tools

• URL: http://arxiv.org/abs/2504.15546v2
• Date: Thu, 01 May 2025 05:50:45 GMT
• Title: A Framework for Testing and Adapting REST APIs as LLM Tools
• Authors: Jayachandu Bandlamudi, Ritwik Chaudhuri, Neelamadhav Gantayat, Kushal Mukherjee, Prerna Agarwal, Renuka Sindhgatta, Sameep Mehta,
• Abstract summary: We present a novel testing framework aimed at evaluating and enhancing the readiness of REST APIs to function as tools for agents.<n>Our framework transforms apis as tools, generates comprehensive test cases for the APIs, tests cases into natural language instructions and evaluates the agent's ability t correctly invoke the API and process its inputs and responses.
• Score: 5.758488787763118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are enabling autonomous agents to perform complex workflows using external tools or functions, often provided via REST APIs in enterprise systems. However, directly utilizing these APIs as tools poses challenges due to their complex input schemas, elaborate responses, and often ambiguous documentation. Current benchmarks for tool testing do not adequately address these complexities, leading to a critical gap in evaluating API readiness for agent-driven automation. In this work, we present a novel testing framework aimed at evaluating and enhancing the readiness of REST APIs to function as tools for LLM-based agents. Our framework transforms apis as tools, generates comprehensive test cases for the APIs, translates tests cases into natural language instructions suitable for agents, enriches tool definitions and evaluates the agent's ability t correctly invoke the API and process its inputs and responses. To provide actionable insights, we analyze the outcomes of 750 test cases, presenting a detailed taxonomy of errors, including input misinterpretation, output handling inconsistencies, and schema mismatches. Additionally, we classify these test cases to streamline debugging and refinement of tool integrations. This work offers a foundational step toward enabling enterprise APIs as tools, improving their usability in agent-based applications.

Related papers

• Test Amplification for REST APIs via Single and Multi-Agent LLM Systems [1.6499388997661122]
  We show how single-agent and multi-agent LLM systems can amplify a REST API test suite.<n>Our evaluation demonstrates increased API coverage, identification of numerous bugs in the API under test, and insights into the computational cost and energy consumption of both approaches.
  arXiv  Detail & Related papers  (2025-04-10T20:19:50Z)
• ToolFactory: Automating Tool Generation by Leveraging LLM to Understand REST API Documentations [4.934192277899036]
  API documentation often suffers from a lack of standardization, inconsistent schemas, and incomplete information.<n>We developed textbfToolFactory, an open-source pipeline for automating tool generation from unstructured API documents.<n>We also demonstrated ToolFactory by creating a domain-specific AI agent for glycomaterials research.
  arXiv  Detail & Related papers  (2025-01-28T13:42:33Z)
• AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL [46.65963514391019]
  AutoRestTest is a novel tool that integrates the Semantic Property Dependency Graph (SPDG) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:54:33Z)
• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs.<n>We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool.<n>Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs [46.65963514391019]
  We present AutoRestTest, the first black-box tool to adopt a dependency-embedded multi-agent approach for REST API testing.<n>Our approach treats REST API testing as a separable problem, where four agents collaborate to optimize API exploration.<n>Our evaluation of AutoRestTest on 12 real-world REST services shows that it outperforms the four leading black-box REST API testing tools.
  arXiv  Detail & Related papers  (2024-11-11T16:20:27Z)
• KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
  KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs. Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
  arXiv  Detail & Related papers  (2024-07-14T14:48:18Z)
• Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios [93.68764280953624]
  UltraTool is a novel benchmark designed to improve and evaluate Large Language Models' ability in tool utilization. It emphasizes real-world complexities, demanding accurate, multi-step planning for effective problem-solving. A key feature of UltraTool is its independent evaluation of planning with natural language, which happens before tool usage.
  arXiv  Detail & Related papers  (2024-01-30T16:52:56Z)
• Leveraging Large Language Models to Improve REST API Testing [51.284096009803406]
  RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation.
  arXiv  Detail & Related papers  (2023-12-01T19:53:23Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs [104.37772295581088]
  Open-source large language models (LLMs), e.g., LLaMA, remain significantly limited in tool-use capabilities. We introduce ToolLLM, a general tool-usetuning encompassing data construction, model training, and evaluation. We first present ToolBench, an instruction-tuning framework for tool use, which is constructed automatically using ChatGPT.
  arXiv  Detail & Related papers  (2023-07-31T15:56:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.