---
title: "Summary: 2026-04-23_16-10-00Z_ToolAttentionIsAllYouNeed_DynamicToolGatingandLazy"
date: 2026-04-23
tags: ['paper', 'research', 'ai']
---
# Summary: 2026-04-23_16-10-00Z_ToolAttentionIsAllYouNeed_DynamicToolGatingandLazy


**Source**: [Original Paper](http://arxiv.org/abs/2604.21816v1)
Saved: 2026-05-08 03:29
Source: 2026-04-23_16-10-00Z_ToolAttentionIsAllYouNeed_DynamicToolGatingandLazy.md
Model: None

---

## Summary
Tool Attention addresses the token overhead of MCP-style tool access by gating tools dynamically and loading schemas lazily. It combines intent overlap scoring, state-aware gating, and compact schema summaries to reduce per-turn tool tokens in a simulated multi-server benchmark.

## Semantic links

## Key Takeaways
- Frames the MCP/Tools Tax as recurring token cost from eager schema injection.
- Uses ISO scoring, gating, and lazy schema promotion to limit context growth.
- Reports large token reductions in simulation, with downstream metrics projected from those measurements.


## Context
The paper targets the context and latency costs of exposing many tools to agentic LLM workflows.

## Implications
Protocol-level tool selection can substantially reduce per-turn overhead in large agent systems.

## Original Reference
- Title: Tool Attention Is All You Need: Dynamic Tool Gating and Lazy Schema Loading for Eliminating the MCP/Tools Tax in Scalable Agentic Workflows
- Authors: Anuj Sadani, Deepak Kumar
- URL: http://arxiv.org/abs/2604.21816v1
- Published: 2026-04-23T16:10:00Z
- Source file: /home/rich/wiki/ai-research/raw/papers/2026-04-23_16-10-00Z_ToolAttentionIsAllYouNeed_DynamicToolGatingandLazy.md

[[raw/papers/2026-05-09_1706.03762-attention-is-all-you-need.md]]

## Related Concepts

- [[concepts/ai-agents/agentic-workflows-hub.md|Agentic Workflows Hub]]
- [[concepts/software-development/software-development-hub.md|Software Development Hub]]
- [[concepts/llm-models/llm-models-hub.md|LLM Models Hub]]
- [[concepts/evaluation-benchmarks/evaluation-benchmarks-hub.md|Evaluation Benchmarks Hub]]