Tool Attention Is All You Need: Dynamic Tool Gating and Lazy Schema Loading for Eliminating the MCP/Tools Tax in Scalable Agentic Workflows
On the map: LLM agents and tool use → Tool learning and function calling
Abstract (arXiv)
The Model Context Protocol (MCP) has become a common interface for connecting large language model (LLM) agents to external tools, but its reliance on stateless, eager schema injection imposes a hidden per-turn overhead the MCP Tax or Tools Tax that practitioner reports place between roughly 10k and 60k tokens in typical multi-server deployments. This payload inflates the key-value cache, is associated with reasoning degradation as context utilization approaches published fracture points around 70%, and turns token budgets into a recurring operational cost. We introduce Tool Attention, a middleware-layer mechanism that generalizes the "Attention Is All You Need" paradigm from self-attention over tokens to gated attention over tools. Tool Attention combines (i) an Intent Schema Overlap (ISO) score from sentence embeddings, (ii) a state-aware gating function enforcing preconditions and access scopes, and (iii) a two-phase lazy schema loader that keeps a compact summary pool in context and promotes full JSON schemas only for top-k gated tools. We evaluate on a simulated 120-tool, six-server benchmark whose per-server token counts are calibrated to public audits of real MCP deployments. In this simulation, Tool Attention directly reduces measured per-turn tool tokens by 95.0% (47.3k -> 2.4k) and raises effective context utilization (a token-ratio quantity) from 24% to 91%. End-to-end figures for task success, latency, cost, and reasoning quality are reported as projections derived from the measured token counts combined with published deployment telemetry; they are not measured on live LLM agents, and we mark projected values explicitly throughout. Taken together, the results support a simple thesis: protocol-level efficiency, not raw context length, is a binding constraint on scalable gentic systems. The code for this work is accessible at https://github.com/asadani/tool-attention
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use. Builds on 11 of its 36 references that are in the map. First posted on arXiv 23 Apr 2026.
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11 of 36 references.
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Other references · 10
- 2017Attention Is All You Needcited by 60,361
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2019Generating Long Sequences with Sparse Transformerscited by 1,546
- 2025NoLiMa: Long-Context Evaluation Beyond Literal Matchingcited by 126
- 2025MindGuard: Intrinsic Decision Inspection for Securing LLM Agents Against Metadata Poisoningcited by 10
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Citing papers per year: 2026: 2.
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- 2025MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
- 2026Action-Space Shaping for LLM Agents: Measuring and Mitigating Tool-Schema Bias
- 2026ContextPipe: Database-Inspired Context Assembly for Long-Horizon Agents
- 2026TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments
- 2025ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering
- 2026ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory