ToolRegistry: A Protocol-Agnostic Tool Management Library for Function-Calling LLMs

Peng Ding · Jul 11, 2025 · Citations: 0

Tool Use

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Best use

Background context only

What to verify

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Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Large Language Model (LLM) applications are increasingly relying on external tools to extend their capabilities beyond text generation. However, current tool integration approaches suffer from fragmentation, protocol limitations, and implementation complexity, leading to substantial development overhead. This paper presents ToolRegistry, a protocol-agnostic tool management system that has evolved from a single library into a modular three-package ecosystem: a core registry for tool management and execution, a server package providing protocol adapters (MCP, OpenAPI) and routing, and a hub package offering curated, production-tested tool implementations. Beyond the original contributions of unified registration, automated schema generation, and dual-mode concurrent execution, the ecosystem now includes an independent MCP client supporting four transport mechanisms, a web-based admin panel for runtime management, an event system for change propagation, and fine-grained tool lifecycle control. Our evaluation demonstrates that ToolRegistry achieves 60-80% reduction in tool integration code, up to 3.1x performance improvements through concurrent execution, and broad compatibility with OpenAI function calling standards. Real-world case studies show significant improvements in development efficiency and code maintainability across diverse integration scenarios. ToolRegistry is open-source and available at https://github.com/Oaklight/ToolRegistry, with comprehensive documentation at https://toolregistry.readthedocs.io/.

Abstract-only analysis — low confidence

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This paper looks adjacent to evaluation work, but not like a strong protocol reference.
The available metadata is too thin to trust this as a primary source.
The abstract does not clearly name benchmarks or metrics.

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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

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What We Could Verify

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