mlsanityData to 14 Oct 2026

Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools

arXiv 2509.11626 ↗First posted 15 Sep 2025cs.SE cs.AIcited by 5

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Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs), is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation by up to 25%. We propose ACE, an automated tool creation and enrichment framework that transforms enterprise APIs into LLM-compatible tools. ACE, (i) generates enriched tool specifications with parameter descriptions and examples to improve selection and invocation accuracy, and (ii) incorporates a dynamic shortlisting mechanism that filters relevant tools at runtime, reducing prompt complexity while maintaining scalability. We validate our framework on both proprietary and open-source APIs and demonstrate its integration with agentic frameworks. To the best of our knowledge, ACE is the first end-to-end framework that automates the creation, enrichment, and dynamic selection of enterprise API tools for LLM agents.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 15 Sep 2025.

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5 of 12 references.

References · 5

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  4. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  5. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 591

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

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  1. 2025A Framework for Testing and Adapting REST APIs as LLM Tools
  2. 2026Agent4Spec: An LLM-based Agentic System to Optimize API Specifications through Automated Validation and Market Analysis
  3. 2026Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
  4. 2026Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation
  5. 2026DADL: A Declarative Description Language for Enterprise Tool Libraries in LLM Agent Systems
  6. 2026The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration
  7. 2025In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents
  8. 2024ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agents