ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering

Daniel Garcia; Dan Roth; Fjona Parllaku; Marianne Menglin Liu; Syed Fahad Allam Shah; Vikas Upadhyay

arxiv: 2510.20036 · v2 · submitted 2025-10-22 · 💻 cs.CL · cs.SE

ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering

Marianne Menglin Liu , Daniel Garcia , Fjona Parllaku , Vikas Upadhyay , Syed Fahad Allam Shah , Dan Roth This is my paper

Pith reviewed 2026-05-18 04:11 UTC · model grok-4.3

classification 💻 cs.CL cs.SE

keywords LLM agentstool selectiontool mergingcontext filteringredundancy reductionbenchmark evaluationagent performance

0 comments

The pith

ToolScope merges redundant tools and filters relevant ones to boost LLM agent tool selection accuracy by 8 to 38 percent.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language model agents rely on many tools but often face redundant ones that overlap in function and names, creating confusion during selection, plus strict context limits that stop them from considering all available tools. The paper proposes ToolScope to fix this by first merging similar tools with an auto-correction step to keep functionality intact, then retrieving only the most relevant tools for a given query to fit within limits. This method is evaluated on three advanced LLMs using three different tool-use benchmarks, where it delivers accuracy gains ranging from 8.38% to 38.6%. A reader would care because more accurate tool use could make these agents practical for harder tasks in real applications without requiring bigger models or more resources.

Core claim

The central discovery is that combining ToolScopeMerger with Auto-Correction to audit and correct merges that reduce redundancy in tool descriptions and names, together with ToolScopeRetriever that ranks and selects pertinent tools for each query, allows compression of toolsets to respect context limits while raising tool selection accuracy from 8.38% to 38.6% on evaluated benchmarks.

What carries the argument

ToolScopeMerger with Auto-Correction for auditing merges and ToolScopeRetriever for context-aware tool ranking and selection.

If this is right

LLM agents achieve higher accuracy in choosing the correct tools for tasks when redundancy is reduced through merging.
Toolsets can be compressed to fit context windows without losing necessary information for selection.
Performance improvements hold across multiple state-of-the-art LLMs and open-source benchmarks.
Real-world toolsets with overlapping tools become more manageable for agent use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The auto-correction mechanism might allow deployment in dynamic environments where tools are added frequently without manual review.
Similar merging and filtering could be adapted for other resource-limited AI systems facing large option sets.
Long-term, this approach may encourage development of standardized tool libraries with built-in deduplication.

Load-bearing premise

That the ToolScopeMerger with Auto-Correction can reliably audit and fix tool merges without introducing functional errors or losing critical tool capabilities.

What would settle it

A demonstration that a merged tool set leads to incorrect task outcomes on a benchmark where the original separate tools succeeded, or that the retriever excludes a tool essential for solving a query.

read the original abstract

Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapping names and descriptions, introducing ambiguity and reducing selection accuracy. LLMs also face strict input context limits, preventing efficient consideration of large toolsets. To address these challenges, we propose ToolScope, which includes: (1) ToolScopeMerger with Auto-Correction to automatically audit and fix tool merges, reducing redundancy, and (2) ToolScopeRetriever to rank and select only the most relevant tools for each query, compressing toolsets to fit within context limits without sacrificing accuracy. Evaluations on three state-of-the-art LLMs and three open-source tool-use benchmarks show gains of 8.38% to 38.6% in tool selection accuracy, demonstrating ToolScope's effectiveness in enhancing LLM tool use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ToolScope pairs auto-corrected tool merging with query-aware retrieval to shrink redundant toolsets for LLM agents, but the accuracy gains hinge on unshown checks that merges preserve original behavior.

read the letter

The core idea is straightforward: merge overlapping tools with an auto-correction pass to cut redundancy, then retrieve only the relevant subset for each query so the agent stays inside context limits. That combination is the main new piece, even if deduplication and retrieval have shown up separately before. The paper runs the pipeline on three LLMs across three standard tool-use benchmarks and reports accuracy lifts between 8 and 38 percent. Those numbers address a real deployment headache when tool libraries grow messy and large, so the work has clear practical intent. Credit for shipping a concrete pipeline that tries to handle both problems at once. The soft spot is the missing validation for the merges themselves. The auto-correction step is supposed to audit and fix them, yet nothing in the description shows systematic checks like running the merged tool against the originals on all parameter combinations or side effects. If some merges quietly drop capabilities, the reported gains could simply reflect fewer choices rather than better selection logic. No error analysis or statistical detail appears in the summary either, which leaves the central claim harder to assess. This is the kind of paper that would interest people building or evaluating LLM agents for real tasks. A reader working on tool-use benchmarks or context compression would get something usable from it. It is grounded enough in empirical results to deserve a serious referee, though the review would probably push hardest on proving the merges stay functionally safe.

Referee Report

2 major / 2 minor

Summary. The paper introduces ToolScope, a system with two components: ToolScopeMerger with Auto-Correction, which automatically merges redundant tools while auditing and fixing issues to reduce ambiguity, and ToolScopeRetriever, which performs context-aware ranking to select only relevant tools and fit within LLM context limits. Experiments across three state-of-the-art LLMs and three open-source tool-use benchmarks report tool selection accuracy gains of 8.38% to 38.6%.

Significance. If the central claims hold, ToolScope could meaningfully improve reliability of LLM agents operating over large, redundant toolsets by addressing both selection ambiguity and context constraints. The multi-model, multi-benchmark evaluation provides a reasonable starting point for assessing practical impact, though the magnitude of gains would be more convincing with stronger controls on merge validity and statistical reporting.

major comments (2)

[§3] §3 (ToolScopeMerger with Auto-Correction): The description states that the auto-correction step audits and fixes merges to preserve functionality, but no concrete validation procedure (e.g., differential testing over parameter spaces, side-effect comparison, or oracle execution traces) is provided. This is load-bearing because the reported accuracy improvements presuppose that merged tools remain behaviorally equivalent to the originals; without such checks, gains could arise simply from a smaller, less ambiguous toolset rather than improved reasoning.
[§5] §5 (Experiments and Evaluation): The abstract and results claim accuracy gains of 8.38–38.6 % yet supply no baseline details, statistical significance tests, variance across runs, or error analysis. In addition, there is no reporting of how many merges were performed, what fraction required auto-correction, or any post-merge capability audit, making it impossible to assess whether the central empirical claim is robust.

minor comments (2)

[Abstract] Abstract: The range of accuracy gains is given without naming the three LLMs or three benchmarks; adding these specifics would improve immediate readability.
[§3] Notation: The distinction between original tool signatures and merged signatures could be illustrated with a short concrete example in the method section to clarify what information is retained or discarded during merging.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [§3] §3 (ToolScopeMerger with Auto-Correction): The description states that the auto-correction step audits and fixes merges to preserve functionality, but no concrete validation procedure (e.g., differential testing over parameter spaces, side-effect comparison, or oracle execution traces) is provided. This is load-bearing because the reported accuracy improvements presuppose that merged tools remain behaviorally equivalent to the originals; without such checks, gains could arise simply from a smaller, less ambiguous toolset rather than improved reasoning.

Authors: We agree that explicit validation of merged-tool equivalence is important for interpreting the accuracy gains. Section 3 describes the auto-correction step as auditing and fixing merges to preserve functionality, but we acknowledge that the current text does not detail the concrete checks performed. In the revised manuscript we will expand §3 to specify the validation procedure: (1) syntactic and semantic compatibility checks on parameters and descriptions, (2) side-effect comparison via execution traces on a representative sample of queries, and (3) differential testing on a held-out set of tool invocations to confirm behavioral equivalence before and after merging. These additions will clarify that the reported improvements arise from reduced ambiguity rather than toolset shrinkage alone. revision: yes
Referee: [§5] §5 (Experiments and Evaluation): The abstract and results claim accuracy gains of 8.38–38.6 % yet supply no baseline details, statistical significance tests, variance across runs, or error analysis. In addition, there is no reporting of how many merges were performed, what fraction required auto-correction, or any post-merge capability audit, making it impossible to assess whether the central empirical claim is robust.

Authors: We concur that fuller experimental reporting is needed. The current manuscript reports aggregate accuracy gains across three LLMs and three benchmarks but does not include the requested details. In the revision we will add: (i) explicit baseline descriptions and comparisons, (ii) statistical significance tests (paired t-tests with p-values), (iii) standard deviation across three independent runs, (iv) error analysis broken down by tool category and query type, and (v) quantitative reporting of the number of merges performed, the fraction that triggered auto-correction, and the results of post-merge capability audits on a sample of tools. These changes will allow readers to better evaluate the robustness of the central claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical method with benchmark validation

full rationale

The paper introduces ToolScopeMerger with Auto-Correction and ToolScopeRetriever as practical techniques for reducing tool redundancy and context length in LLM agents. Claims of 8.38–38.6% accuracy gains are grounded in direct evaluations on three LLMs and three open-source benchmarks. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the described chain. The central results are falsifiable experimental outcomes rather than identities or self-referential constructions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no identifiable free parameters, axioms, or invented entities; the approach appears to rely on standard LLM prompting and retrieval techniques.

pith-pipeline@v0.9.0 · 5694 in / 1038 out tokens · 31174 ms · 2026-05-18T04:11:17.268361+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

ToolScopeMerger ... graph-based framework that consolidates overlapping tools ... Auto-Correction stage uses an LLM validator to audit clusters
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

ToolScopeRetriever ... hybrid retrieval score ... α·sdense + (1-α)·ssparse ... reranked using a cross-encoder

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
cs.SE 2026-04 accept novelty 5.0

LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project
cs.LG 2026-03 unverdicted novelty 5.0

The Workload-Router-Pool architecture is a 3D framework for LLM inference optimization that synthesizes prior vLLM work into a 3x3 interaction matrix and proposes 21 research directions at the intersections.