Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

Ruocheng Guo; Kaiwen Dong; Xiang Gao; Kamalika Das

cs.AIFeb 23, 2026

Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

Authors: Ruocheng Guo, Kaiwen Dong, Xiang Gao, Kamalika Das

Organizations: Intuit AI Research, Mountain View, CA, USA

Abstract

While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agent performance increasingly plateaus due to the quality of the tool interfaces these agents consume. Tool descriptions are often written for human developers and tolerate ambiguity that agents cannot resolve, particularly as the number of candidate tools grows. Existing approaches to improving tool interfaces (1) require re-running a multi-stage per-tool pipeline - synthesizing queries, executing an agent to collect trajectories, annotating trajectories, and prompting a strong LLM multiple times - for every API that enters the catalog, and (2) typically optimize each tool independently, limiting scalability and generalization to unseen tools. We propose Trace-Free+, a curriculum learning framework that progressively transfers supervision from trace-rich settings to trace-free deployment, encouraging the model to internalize reusable patterns of what makes a tool description effective. To support this approach, we construct a large-scale dataset of high-quality tool interfaces derived from real-world APIs through a principled data synthesis workflow. Experiments on widely adopted benchmarks show that Trace-Free+ improves robustness as tool catalogs scale to 150+ candidates - in scaling experiments, reducing accuracy degradation by 29.23% and improving average query-level success by 60.89% on StableToolBench - generalizes across domains without retraining, and provides complementary gains on top of agent fine-tuning.

Figures & tables

Figure 1: An illustration of the proposed tool interface improvement pipeline. Compared to the original description ( $D_{0}$ ), the learned description generator produces more effective tool descriptions that lead to better tool usage.

	Multi-Step (G2+G3)								Single-Step (G1)
	G2 Category		G2 Instruction		G3 Instruction		Avg		G1 Category		G1 Instruction		G1 Tool		Avg
	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL
Trace-Free+	68.7 ± 0.7	50.8 ± 0.0	71.4 ± 1.1	47.0 ± 0.1	63.9 ± 1.8	36.1 ± 0.1	68.0	44.6	73.8 ± 0.7	65.6 ± 1.0	72.6 ± 1.5	60.0 ± 1.0	70.0 ± 1.4	56.4 ± 1.6	72.1	60.7
Trace-Free	66.4 ± 1.2	44.1 ± 0.0	69.2 ± 4.0	46.4 ± 3.6	59.9 ± 1.2	41.8 ± 1.2	65.2	44.1	71.8 ± 2.3	62.7 ± 2.1	70.8 ± 0.1	60.8 ± 0.9	68.5 ± 2.7	53.6 ± 2.8	70.4	59.0
D1	67.9 ± 1.2	48.5 ± 0.1	67.4 ± 1.0	45.5 ± 0.1	45.2 ± 4.2	30.6 ± 0.1	60.2	41.5	75.5 ± 1.3	64.9 ± 1.3	74.7 ± 0.7	66.1 ± 0.6	68.0 ± 0.6	53.7 ± 0.5	72.7	61.6
D0	68.4 ± 0.3	39.0 ± 0.0	67.8 ± 0.8	43.9 ± 0.1	50.7 ± 0.2	17.6 ± 0.1	62.3	33.5	73.0 ± 0.3	62.4 ± 1.5	72.8 ± 0.4	62.3 ± 0.9	71.0 ± 0.9	52.8 ± 1.1	72.3	59.2

Table 1: Trace-free evaluation on StableToolBench for multi-step (G2+G3) and single-step (G1) subsets. SL/QL: subtask/query-level success rate. GPT-4.1 is the tool-using agent.

	RestBench
	TMDB		Spotify
Method	SL	QL	SL	QL
Trace-Free+	88.1 ± 0.4	74.9 ± 0.5	68.1 ± 0.3	49.3 ± 0.6
Trace-Free	78.4 ± 1.1	57.7 ± 1.2	65.0 ± 1.6	44.7 ± 2.8
D1	78.2 ± 0.1	58.0 ± 0.8	65.1 ± 0.8	45.7 ± 1.8
D0	69.8 ± 0.2	49.5 ± 0.2	57.1 ± 2.9	34.9 ± 2.1

Table 2: Trace-free evaluation results on RestBench – TMDB and Spotify. SL: subtask-level, QL: query-level. No tool execution traces are available at inference time.

Figure 3: Scaling experiment results on the more challenging G2-G3 subsets of StableToolBench. We report query-level (QL) results.

Setting	StableToolBench	RestBench
Base + D0	37.4	54.1
Base + Trace-Free+	40.1 (+ 10.7%)	62.1 (+ 14.8%)
Fine-tuned + D0	40.0 (+ 10.7%)	55.0 (+ 1.0%)
Fine-tuned + Trace-Free+	41.8 (+ 11.7%)	62.9 (+ 16.3%)

Table 3: Query-level success rate with base and fine-tuned Qwen3-4B-Instruct agents.

Appendix figures & tables8 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Queries	Tools	Tools per Query
RestBench
TMDB	100	54	54
Spotify	57	40	40
StableToolBench
G1 Category	153	364	4.21
G1 Instruction	163	820	5.29

Appendix

Table 4: Descriptive statistics of evaluation benchmarks. For StableToolBench, we only consider their solvable queries ( Guo et al., 2024 ) .

Dataset	Queries	Tools	Tools per Query
Synthesized SFT Data
$\mathcal{A}_{ts}$	4,726	4,585	6.81
$\mathcal{A}_{tr}$	2,189	991	9.21

Appendix

Table 5: Statistics of the dataset we created in Sec 3.2 for tool interface improvement. To ensure evaluation on entirely unseen tools, all tools appearing as candidates in StableToolBench test queries are assigned to the test set

\mathcal{A}_{ts}

and the remainder form

\mathcal{A}_{tr}

Parameter	SFT
Base Model	Qwen3-4B-Instruct-2507
Hardware	1 $\times$ 8-A100 GPU Node
Optimizer	AdamW
Learning Rate	$5.0\times 10^{-5}$
LR Scheduler	Cosine
Training Epochs	2.0

Appendix

Table 6: Hyperparameters for SFT.

#	Category	Level / Key Test	Explanation	Example
1	Tool selection scope	Tool-level. Does it help an agent decide between multiple options?	Explicitly states when to use this tool vs. another, when NOT to use it, or compares it to a similar tool. Must go beyond a simple purpose statement.	“Use this API when you need to retrieve a list of artworks by search query. Do not use it if you require detailed metadata such as dimensions or provenance. For more detail, use Detect Features instead.”
2	Cross-tool dependencies	Tool-level. Does it name a specific upstream endpoint?	A parameter value must come from calling a specific named tool or endpoint first. The description explicitly names the other tool/endpoint — not just general prior state.	“The hash parameter must be extracted from the Check Status API response when movie_status equals DONE, and vsid must come directly from the Start Movie Session API response.”
3	Output description	Tool-level. Does it enumerate response fields or explicitly state what is NOT returned?	Explicitly enumerates response fields, data types, or structure — or explicitly states what is not included. Does not apply to general purpose statements that merely describe what the tool retrieves.	“Returns all static metadata including logo, description, official website URL, and social links. Does not include videos, subcategories, or category-specific details.”
4	Parameter constraints	Parameter-level. Does it restrict valid values or structure?	Restricts what values a parameter can take or specifies how it must be structured: enums, numeric ranges, case sensitivity rules, date formats, encoding schemes, separator conventions, or array structure.	“Must be one of en (English) or nl (Dutch). limit must be a positive integer between 1 and 100. date must be in YYYY-MM-DD format. image must be a Base64-encoded string.”
5	Cross-param dependencies	Parameter-level. Does it constrain one parameter based on another?	Constraints between parameters within the same tool — parameters that must be paired together or are mutually exclusive.	“ longitude (requires lat ).”

Appendix

Table 7: Definitions of the five interface pattern categories used to classify tool description improvements. Categories 1–3 are tool-level (apply to the description as a whole); Categories 4–5 are parameter-level (apply per parameter).

#	Category	$D_{0}$	$D_{1}$	$D_{2}$	Trace-Free+
1	Tool selection scope	3.3%	99.7%	100.0%	97.2%
2	Cross-tool dependencies	0.3%	32.0%	21.1%	30.0%
3	Output description	11.6%	98.6%	100.0%	27.0%
4	Parameter constraints	9.9%	87.9%	89.5%	94.2%
5	Cross-param dependencies	0.3%	8.5%	21.1%	17.1%

Appendix

Table 8: Percentage of tool descriptions covering each of the five interface pattern categories, comparing

D_{0}

D_{1}

D_{2}

, and Trace-Free+.

D_{0}

descriptions are consistently sparse across all categories; all three improved variants close most of the gap on scope and parameter constraints, with differences concentrated on cross-tool dependencies and output description.

	$D_{0}$ (Original)	Trace-Free+ (Ours)
API	Walk Score (provider: Walk Score )
Parameters	lat (str), lon (str), address (str), wsapikey (str), bike (str, opt.), transit (str, opt.), format (str, opt.)
Description	Get Walk Score. address : “The URL encoded address.” lat / lon : no range stated. format : “Type of result to return: (movie, series, episode).”	Retrieves Walk, Transit, and Bike scores for a location. lat must be a decimal in $[-90,90]$ ; lon in $[-180,180]$ . address must not be URL-encoded. bike and transit must be exactly ’1’ — not ’true’ or ’on’ . format must be ’json’ or empty (XML default); any other value is rejected. Do not use for real-time traffic data or historical trends.
Patterns	—	Parameter constraints ( lat / lon numeric ranges; exact-value flags for bike , transit , format ; format correction for address ); Tool selection scope (exclusions: not for real-time traffic or historical trends)
API	Player Trophy Information (provider: Trackmania )
Parameters	player_id (str), limit (float, opt.), page (float, opt.)

Appendix

Table 9: Three examples of Trace-Free+-generated descriptions for unseen tools compared against

D_{0}

. In all cases the model infers constraints without execution traces.

	StableToolBench
	G1 Category		G1 Instruction		G1 Tool		G2 Category		G2 Instruction		G3 Instruction
Ratios	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL	SL	QL
(0.1, 0.9)	73.8 ± 0.7	65.6 ± 1.0	72.6 ± 1.5	60.0 ± 1.0	70.0 ± 1.4	56.4 ± 1.6	68.7 ± 0.7	47.5 ± 0.8	71.4 ± 1.1	48.3 ± 1.6	63.9 ± 1.8	46.4 ± 4.1
(0.3, 0.7)	73.7 ± 1.9	63.6 ± 2.8	73.2 ± 2.3	61.3 ± 4.3	68.3 ± 1.1	52.8 ± 0.7	68.6 ± 0.0	47.9 ± 0.6	69.1 ± 2.9	45.4 ± 3.6	61.1 ± 3.0	41.8 ± 3.5
(0.5, 0.5)	75.8 ± 0.0	66.0 ± 0.0	71.1 ± 0.0	64.3 ± 0.0	69.2 ± 0.0	55.6 ± 0.0	65.9 ± 0.0	48.3 ± 0.0	68.9 ± 0.0	44.9 ± 0.0	59.8 ± 0.0	39.3 ± 0.0
Trace-Free	71.8 ± 2.3	62.7 ± 2.1	70.8 ± 0.1	60.8 ± 0.9	68.5 ± 2.7	53.6 ± 2.8	66.4 ± 1.2	44.1 ± 0.0	69.2 ± 4.0	46.4 ± 3.6	59.9 ± 1.2	41.8 ± 1.2

Appendix

Table 10: Parameter Study for Trace-Free+ in Trace-free Evaluation

	Non-Live		Live
Model	D0	+ Trace-Free+	D0	+ Trace-Free+
GPT-4.1	88.56	89.04	79.28	80.63
Claude Sonnet 4.5	63.11	65.50	52.40	53.45
Gemini-3-pro-preview	91.24	91.55	84.98	86.41

Appendix

Table 11: Transfer evaluation on BFCLv2. We apply Trace-Free+ to improve tool descriptions and evaluate on the Non-Live and Live splits.

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