The Domain-Oriented Tooling Pattern is proposed: instead of generating SQL at query time, the model selects from a small set of domain-aligned tools whose parameterized queries encapsulate schema navigation, joins and business rules on the server side.
Abstract
Agents built on Large Language Models (LLMs) increasingly reach enterprise data through the Model Context Protocol (MCP), and many MCP database servers maximize flexibility by exposing a single generic SQL execution tool. This paper proposes the Domain-Oriented Tooling Pattern: instead of generating SQL at query time, the model selects from a small set of domain-aligned tools whose parameterized queries encapsulate schema navigation, joins and business rules on the server side. We formalize the pattern around three architectural invariants and introduce Model Demotion, the observation that replacing SQL synthesis with intent classification lowers the model tier required to serve routine requests. As a reference implementation we present MCP Blueprint, an open-source framework in which domain tools are defined declaratively as YAML metadata plus external parameterized SQL files. We evaluate the pattern with a public reproducibility benchmark comparing three MCP server designs - raw SQL execution, a thin generic tool pack, and a verticalized domain pack - on four local models (3B-8B) across seventeen customer-facing tasks over the Sakila database (609 completed cells; temperature 0; three repetitions per cell). The verticalized pack reaches a pooled mean score of 0.939 versus 0.666 for raw SQL and 0.605 for the generic pack; the smallest model improves from 0.583 to 0.929, matching or exceeding every larger configuration while cutting cost per correct answer by an order of magnitude. All harness code, prompts, gold answers, frozen packs and per-cell results are publicly available.
This work presents SCOUT (Selective Context Optimization for Universal Tooling for Universal Tooling), which reframes tool exposure as a context-selection problem, injecting only tools relevant to the current step, and reduces MCP tool-token consumption by 99%, cutting per-query inference cost at enterprise scale.
Olympia Saha, Amy Wang, Srinivasan Manoharan· 0 citations
DBcover is proposed, an LLM-driven database test generation framework that performs white-box, code-aware SQL test generation through contextual reasoning, and substantially outperforms existing fuzzers.
Yan-Kai Rong, Shuang Liu, Jin-Hao Dong et al.· Proceedings of the 2026 IEEE...· 0 citations
This work presents AutoSQL, a system that reconstructs SQL templates from Go ORM code that constructs a Code Index, a directed graph that captures structural dependencies between functions, types, and global variables as navigable edges and synthesizes SQL templates.
Jun-Song Pu, Yi-Chen Li, Zhuang-Bin Chen et al.· Proceedings of the 41st IEEE...· 0 citations
SafeQL is proposed, a search-based refinement paradigm that redefines the role of the DBMS as an active guide in the refinement process, and significantly improves execution accuracy and efficiency compared to regeneration-based methods.
Geonho Lee, Min-Soo Kim· Proceedings of the VLDB Endo...· 0 citations
This article investigates serialization-boundary optimization as a critical architectural concern for MCP-native systems and proposes four composable backend design patterns that restructure API responses to maximize semantic density while minimizing token consumption without modifying underlying domain models or persi...
Nikhil Bharadwaj Ramashasthri· International Journal of Eng...· 0 citations
A feedback-driven lifecycle for SQL optimization, based on empirically validated query rewrites, which supports a source-neutral lifecycle in which alternative SQL formulations are admitted, measured, retained, activated, or rejected according to accumulated evidence rather than the generating mechanism.
Martin Kostov, K. Kaloyanova· Electronics· 0 citations
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