# ToolChoiceConfusion: Causal Minimal Tool Filtering for Reliable LLM Agents

arXiv: https://arxiv.org/abs/2606.06284 · HTML: https://mlsanity.com/paper/2606.06284/

## Key facts

- Cited by 9 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 10 of its 17 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models](https://mlsanity.com/paper/2503.01763/) (2025)
- [RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.03275/) (2025)
- [ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering](https://mlsanity.com/paper/2510.20036/) (2025)
- [How Many Tools Should an LLM Agent See? A Chance-Corrected Answer](https://mlsanity.com/paper/2605.24660/) (2026)
- [Self-Healing Agentic Orchestrators for Reliable Tool-Augmented Large Language Model Systems](https://mlsanity.com/paper/2606.01416/) (2026)

## Built on by (most cited first, 9 of 9)

- [Lingering Authority: Revocable Resource-and-Effect Capabilities for Coding Agents](https://mlsanity.com/paper/2606.22504/) (2026)
- [Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents](https://mlsanity.com/paper/2606.13884/) (2026)
- [Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents](https://mlsanity.com/paper/2606.07904/) (2026)
- [ToolMenuBench: Benchmarking Tool-Menu Filtering Strategies for Reliable and Efficient LLM Agents](https://mlsanity.com/paper/2606.15508/) (2026)
- [Entity Binding Failures in Tool-Augmented Agents](https://mlsanity.com/paper/2606.30531/) (2026)
- [GIST-CMTF: Goal-State Inference for Causal Minimal Tool Filtering in LLM Agents](https://mlsanity.com/paper/2606.16813/) (2026)
- [Binding Drift in Multi-Step Tool-Augmented Agents](https://mlsanity.com/paper/2607.18316/) (2026)
- [Don't Offer What Can't Be Done: Deterministic Executability Gating for LLM Skill Selection at Scale](https://mlsanity.com/paper/2608.01050/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

Large language model agents increasingly rely on external tools, but larger tool menus can reduce reliability and efficiency by increasing wrong-tool calls, premature actions, and token cost. Existing tool-selection methods often optimize semantic relevance, exposing tools whose names or descriptions match the user request. We argue that relevance is insufficient: a tool may be related to the task while still being unnecessary or premature at the current step. We propose Causal Minimal Tool Filtering (CMTF), a training-free method that selects tools by causal sufficiency. CMTF uses lightweight precondition-effect contracts to expose only the minimal next-step tool frontier needed to advance from the current state toward the user goal. Across multi-step tool-use tasks, we compare CMTF with all-tools exposure, keyword retrieval, state-aware filtering, and causal-path ablations, measuring task success, wrong-tool calls, premature actions, tool exposure, and token cost. In the main benchmark with 102 tasks, 100 tools, four LLM backends, and 2448 task-method-model runs, CMTF matches the strongest causal baseline in aggregate success while reducing visible tools from 100 to one per step and reducing token usage by about 90% relative to all-tools exposure.