Tool-Completion Benchmark

• Tool-Completion Benchmark is a structured protocol and dataset designed to assess LLMs' ability to plan, generate, and execute API calls and code modifications in complex, real-world scenarios.
• It employs realistic code contexts, multi-hop reasoning, and error recovery challenges using data from actively maintained repositories across several programming languages.
• The benchmark utilizes detailed evaluation metrics—including unit test pass rates, tool selection accuracy, and execution efficiency—to offer granular insights into model performance.

A Tool-Completion Benchmark provides a structured protocol and dataset for evaluating the ability of models—primarily LLMs and code-focused LLMs—to plan, generate, and execute API function calls, code insertions, or tool actions in response to complex real-world prompts. These benchmarks measure not just final solution correctness but also tool selection, parameterization, execution order, and error recovery, closely mirroring industrial usage scenarios in software development, agentic reasoning, and multimodal settings.

1. Historical Context and Motivation

Traditional code-completion and tool-use benchmarks focused on function or class-level code generation conditioned on verbose, human-readable descriptions (e.g., HumanEval, MBPP), or on short, synthetic multi-turn tool-use tasks with explicit tool names. These settings fail to capture production-level requirements: fill-in-the-middle completions, multi-hop tool orchestration, tool planning and scheduling, and the compatibility of completions with the ever-evolving environments (e.g., changing APIs, complex IDE workflows, and multimodal grounding) (Wu et al., 7 Aug 2024, Huang et al., 30 Jan 2024, Farn et al., 2023, He et al., 6 Oct 2025, Zhou et al., 21 Nov 2025).

Increasing industrial demand for intelligent assistants and autocompletion tools has catalyzed a new wave of tool-completion benchmarks that prioritize repository-level code, real-world tool chains, rigorous test execution, and granular diagnostics of reasoning pathways and API misuse. These benchmarks aim to predict real developer acceptance, uncover agent failure modes, and motivate improvements in LLM design, training, and deployment.

2. Dataset Construction and Scenario Design

Modern tool-completion benchmark datasets are constructed to ensure realism, complexity, and reproducibility:

• Code Completion Contexts: Datasets select real, actively-maintained repositories (Python, TypeScript, Java, etc.) filtered to avoid training-data contamination. Contextual prompts are constructed by masking code blocks covered by true unit tests and snapshotting realistic file-local and repository-wide references (Wu et al., 7 Aug 2024, Pan et al., 2 Oct 2024, Kuhar et al., 19 Nov 2024, Yang et al., 16 Dec 2024, Liu et al., 2023).
• Tool Use and Multi-Hop Reasoning: Benchmarks such as UltraTool and TRAJECT-Bench define a structured triplet: natural-language query, tree- or chain-structured plan, and a toolset (JSON schema per tool). Queries demand multi-step planning, cross-tool execution, or multi-hop graph traversals, possibly in multimodal or personalized settings (e.g., FamilyTool’s knowledge-graph queries) (Huang et al., 30 Jan 2024, He et al., 6 Oct 2025, Wang et al., 9 Apr 2025, Zhou et al., 21 Nov 2025).
• Dialog-Driven and Multimodal Evaluations: ToolTalk, M³-Bench, and MTU-Bench simulate complex conversational or visual scenarios, requiring agents to ground tool choices in external observations, image recognition, or persistent intermediate resources (Farn et al., 2023, Zhou et al., 21 Nov 2025, Wang et al., 15 Oct 2024).
• Error Surface Enrichment: Hard and nested cases, OOD dialogues, inductive KG splits, and error-focused task variations are systematically included to challenge generalization, compositionality, and robustness (Kokane et al., 20 Nov 2024, Wang et al., 9 Apr 2025, Wang et al., 15 Oct 2024).

3. Evaluation Protocols and Metrics

Tool-completion benchmarks deploy a suite of precise, often multi-dimensional metrics at both global and stepwise granularity:

Metric Category	Example Metrics
Correctness/Success	Pass@k (unit test pass rate), Exact Match (trajectory/call), Task Completion Score, API-Completion F1, Semantic Correctness
Trajectory Alignment	Trajectory Inclusion, Tool Selection Accuracy, Parameter Filling Accuracy, Dependency/Order Correctness, Stepwise Process Supervision
Structure and Semantics	Argument Similarity (ArgSim), Step Coherence, Merge Purity, Order Consistency (M³-Bench)
Efficiency/Scheduling	Turn Efficiency, Execution Time, Token/Action Budget (TPS-Bench)
Diagnostic Errors	Error rates: Insufficient Calls, Hallucinated Function Names, Invalid Format, Incorrect Arguments, Redundant Calls (ToolScan)

Evaluation often combines executable, deterministic matching (via strict JSON or AST diffing, test execution) with advanced labeling such as LLM-judge scoring (e.g., trimmed means over 4 judge models, as in M³-Bench), and process supervision at each reasoning step (ToolComp) (Wu et al., 7 Aug 2024, Nath et al., 2 Jan 2025, Kokane et al., 20 Nov 2024, Zhou et al., 21 Nov 2025).

The implementation of error taxonomies and process labels enables detailed diagnostics and attribution of model failure modes. For example, process supervision allows not only final-outcome accuracy but also step-wise correctness and interventional training (Nath et al., 2 Jan 2025, He et al., 6 Oct 2025, Kokane et al., 20 Nov 2024).

4. Benchmark Systems, Regions of Difficulty, and Comparative Analysis

• Model Families: Benchmark evaluations span closed-source LLMs (GPT-4, GPT-4o, Claude, Gemini, GLM), open-source code LLMs (CodeQwen, StarCoder, Qwen2.5-Coder, DS-Coder, LLaMA), as well as agentic variants trained or fine-tuned specifically for tool use (e.g., ToolLLaMA, MTU-LLaMA).
• Difficulty Splits: Benchmarks segment tasks by type (single vs. multi-tool, single vs. multi-turn, easy vs. hard, OOD, sequential vs. parallel, inductive vs. transductive, multi-hop depth), allowing for scaling and ablation analysis. Task completion rates often drop sharply with increasing hop length (e.g., 88.8%→45.2% as hops increase from 1→3 in FamilyTool) or longer chains (TRAJECT-Bench EM collapses between 3–5 steps) (Wang et al., 9 Apr 2025, He et al., 6 Oct 2025).
• Failure Mode Surfaces: Most systems demonstrate robust performance on short, single-tool, or well-instrumented tasks, but bottleneck dramatically on nested, chained, or reasoning-rich scenarios, frequently hallucinating tool/API names, misparameterizing calls, or misordering dependent actions. Larger, code-specialized models reduce but do not eliminate these effects; fine-tuned/open-source models can approach closed-source baselines on narrow axes (e.g., single-tool completion) (Wu et al., 14 May 2024, Wang et al., 15 Oct 2024, Zhou et al., 21 Nov 2025).

5. Impact on Model Training and Agent Engineering

Insights from tool-completion benchmarks have shaped both evaluation and model development practices:

• Correlation with Production/Acceptance: Pass@1 rates and fine-grained metrics from repository-level code benchmarks (e.g., RepoMasterEval, Codev-Bench) exhibit high correlation with real developer acceptance rates and model productivity in industrial deployment. Changes in HumanEval or synthetic benchmarks correlate poorly with real-world user metrics (Wu et al., 7 Aug 2024, Pan et al., 2 Oct 2024).
• Guided Fine-tuning: Multi-stage or process-labeled supervision (ToolComp’s process-reward models, MTU-Instruct’s chain-of-thought traces) and inclusion of grammar-aware instruction datasets (Repo-Instruct, Seal-Tools) significantly boost tool-completion reliability, chain-of-thought planning, and robustness in agentic workflows (Nath et al., 2 Jan 2025, Yang et al., 16 Dec 2024, Wu et al., 14 May 2024).
• Error Mitigation Strategies: Process supervision, explicit JSON-format constraints, self-reflection/planning prompts, feedback-based recovery, and hybrid dynamic retrieval/inference loops have emerged as best practices for mitigating hallucination, misbinding, and misordering errors (Kokane et al., 20 Nov 2024, He et al., 6 Oct 2025, Xu et al., 3 Nov 2025).
• Scheduling and Efficiency: Benchmarks such as TPS-Bench explicitly measure not just correctness but efficiency (latency, number of tool-call rounds), highlighting the need for dynamic mixing of parallel and sequential execution strategies, and the applicability of RL or preference optimization in tool scheduling (Xu et al., 3 Nov 2025).

6. Open Challenges and Directions

Remaining challenges and recommended future directions include:

• Multi-Language, Multimodal, and Inductive Evaluation: Most repository-level benchmarks remain limited to Python/TypeScript; multi-language, cross-modal, and dynamically evolving or personalized setting support is limited (FamilyTool, M³-Bench) (Wang et al., 9 Apr 2025, Zhou et al., 21 Nov 2025).
• Long-Tail/Extended Reasoning: Model performance sharply degrades with trajectory length or hop complexity—addressing incomplete tool graphs and deep chains remains open (He et al., 6 Oct 2025, Nath et al., 2 Jan 2025).
• Generality and Adaptation: Inductive and out-of-distribution splits expose model brittleness: models fail to generalize correctly to new APIs, tool signatures, or unseen knowledge-graph edges (Wang et al., 9 Apr 2025, Wang et al., 15 Oct 2024).
• Richness of Realistic Test Suites: Most published datasets rely on synthetic tests or partial coverage; benchmarks such as RepoMasterEval and Codev-Bench demonstrate that mutation testing and iterative test augmentation are essential for surfacing real-world failure cases (Wu et al., 7 Aug 2024, Pan et al., 2 Oct 2024).
• Structure-Conscious Metrics and Transparency: Metric suites that decompose success into coverage, semantic fidelity, and workflow consistency provide deeper insight; interpretability of model decision traces and agent rollouts is still limited in many pipelines (Zhou et al., 21 Nov 2025, He et al., 6 Oct 2025).

7. Representative Benchmark Comparison Table

Benchmark	Domain	Notable Features
RepoMasterEval	Real-world code completion	Mutation-tested, repository-level, Pass@1,
UltraTool	Planning, creation, usage	Tree-structured plans, toolset creation, LLM-judge metrics
Seal-Tools	Tool-calling	Self-instruct, nested calls, schema-conformant, F1 metrics
ToolTalk	Conversational tool-use	Dialogue, multi-tool, precision/recall/errors
Codev-Bench	Developer-centric code	Unit test passing, agentic sample collection, fine-grained contexts
ExecRepoBench	Executable repository code	Grammar-based masking, multi-level AST units
ToolComp	Multi-tool reasoning	Step-wise process labels, trajectory/step metrics
ToolScan	Tool-call error analysis	7 error types, feedback loop, per-pattern metrics
TPS-Bench	Tool planning & scheduling	Scheduling efficiency, parallel vs sequential, RL ablation
TRAJECT-Bench	Trajectory-aware tool use	Breadth/depth, tool-order, argument accuracy
M³-Bench	Multimodal tool execution	Visual grounding, MCP protocol, Hungarian alignment metrics

The continual evolution of tool-completion benchmarks is a pivotal factor in progressing the robustness, interpretability, and industrial applicability of both code-focused and general-purpose LLM agents (Wu et al., 7 Aug 2024, Huang et al., 30 Jan 2024, He et al., 6 Oct 2025, Zhou et al., 21 Nov 2025).