🤖 AI Summary
This work addresses the lack of structured, verifiable, and governable tool support for large language model (LLM) agents in operational tasks, where existing approaches are often static or manually integrated, struggling to balance security and extensibility. The authors propose the “Tool Capsule” paradigm, which encapsulates tools as standardized units comprising intent, contract, implementation, policy, and verification evidence. They design an efficient intent-scoped routing mechanism enabling on-demand, secure tool invocation. The system integrates a sandboxed verification pipeline, MCP-compatible routing, credential binding, and lifecycle governance. Experiments demonstrate a micro F1 score of 0.901 across 83 routing tests with a 99.2% reduction in context overhead; all 25 end-to-end tasks produced valid toolkits (micro F1 = 0.940), with 23 successfully passing real-time sandbox validation.
📝 Abstract
Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting inside enterprise systems. Yet the tool layer on which this execution depends is still commonly treated as either a hand-written integration artifact or a static list of schemas exposed to a model. This paper introduces Tool Forge, a validation-carrying toolchain for converting natural-language capability intent into governed, sandbox-verified, cataloged tool artifacts and exposing those artifacts to agents through a token-efficient routing layer.
Tool Forge treats a tool as a capsule containing intent, capability contract, implementation, dependency policy, tests, documentation, runtime validation evidence, lifecycle state, credential bindings, and routing metadata. It also introduces a Router that exposes intent-scoped tool sessions instead of loading full catalog schemas into the model context. We describe the system architecture, validation pipeline, MCP-facing routing model, governance controls, and initial reproducible benchmarks from the open-source implementation.
Across 83 Router benchmark cases, Tool Forge Router achieves aggregate micro-F1 of 0.901 while reducing estimated task-flow tool context by 99.2% relative to naive full-catalog schema exposure. In a 25-case end-to-end generation probe over local-tool tasks, Tool Forge generates 25 of 25 tool bundles, reaches micro-F1 of 0.940 against deterministic acceptance checks, and passes 23 of 25 live sandbox validations. These results are presented as an initial systems benchmark, not as a state-of-the-art claim. The paper identifies remaining challenges in adversarial routing, broader API grounding, sandbox isolation, and cross-system evaluation.