TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

TSCG is a deterministic compiler that converts JSON tool schemas into structured text optimized for language model interpretation, solving a critical failure point in agentic AI systems. The technology restores accuracy in smaller models (4B-14B) from near-zero to 84%+ on production-scale tool catalogs while reducing token consumption by 52-57%, shipping as a lightweight TypeScript package.

The emergence of TSCG addresses a fundamental architectural problem in production AI agent frameworks: the mismatch between machine-readable JSON schemas and language model comprehension. While frameworks like OpenAI Function Calling and Anthropic Tool Use transmit tool specifications as JSON, smaller language models struggle to parse and utilize these formats effectively at scale, causing failures that disproportionately affect resource-constrained deployments.

This research represents a maturation of the agentic AI infrastructure layer. As organizations deploy increasingly sophisticated multi-tool agents, the bottleneck has shifted from model capability to representation efficiency. TSCG's deterministic approach—requiring no model access, fine-tuning, or runtime search—provides a deployable solution that works across different model families, though with nuanced effectiveness profiles. The research reveals that different frontier models respond distinctly to formatting choices (Opus favors operator density, GPT-5 requires sensitivity to operator count, Sonnet shows robustness), enabling informed deployment decisions.

For the AI infrastructure market, TSCG validates that significant performance gains remain available through representation optimization rather than raw model scaling. The 52-57% token savings directly reduce inference costs, while the accuracy restoration on production MCP schemas (complex, real-world tool catalogs) demonstrates practical relevance beyond synthetic benchmarks. The zero-dependency TypeScript implementation lowers adoption friction, positioning this as a standard tool in the agent deployment pipeline.

Looking forward, this signals growing specialization within AI infrastructure: as base model capabilities plateau, optimization layers targeting specific failure modes become competitive advantages. Whether TSCG becomes a de facto standard or inspires similar representation-layer solutions will indicate whether the industry converges on universal formatting principles for agentic systems.

• →TSCG restores accuracy in small language models from 0% to 84.4% on 20-tool deployments through deterministic schema reformatting without model retraining.
• →Different frontier models exhibit distinct operator sensitivity profiles, enabling per-model deployment optimization rather than one-size-fits-all approaches.
• →Token efficiency improvements of 52-57% reduce inference costs significantly while maintaining or improving accuracy on production-scale MCP schemas.
• →The solution requires no model access, fine-tuning, or runtime search, making it immediately deployable as a lightweight API-boundary layer.
• →Synthetic benchmark results generalize to real-world schemas within 0.1 accuracy points, validating the approach's practical production readiness.