Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools

Atul Anand; Sourav Chattaraj

REVIEW 2 major objections 3 minor 27 references

Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools

T0 review · 2 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Canary tools turn a single 'wrong tool' outcome into a six-way diagnosis of which tool-selection reasoning step failed.

desk verdict A genuinely useful new diagnostic for tool-selection failures, carefully evaluated, but the typed-attribution inference is weaker for the two LLM-rewritten canary types than the paper's wording suggests. read the letter →

arxiv 2608.04719 v1 pith:LQN7OZYF submitted 2026-08-05 cs.AI

Atul Anand , Sourav Chattaraj This is my paper

classification cs.AI

keywords canarytoolstoolselectionLLMagentsMCPdiagnosticevaluationcapabilitymiragetool-usereasoningsusceptibilityrate

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Standard agent benchmarks score a wrong tool call as one bit: it failed, but not why. This paper introduces canary tools — probe tools planted in an agent's tool set, each engineered as a targeted perturbation of a real tool along a single axis — so that calling a canary reveals which specific reasoning weakness caused the wrong pick. On eight models and 8,640 task runs, susceptibility to these probes spans a 36-fold range, capability tier does not predict safety, and the six probe types fire at different points on the capability curve, with 'capability mirage' the only type that reliably traps strong models. Because softening the give-away phrases leaves frontier trap rates essentially unchanged, the authors argue the probes measure tool-selection reasoning, not phrase-spotting.

What carries the argument

The load-bearing object is the canary tool: a targeted perturbation c = δ(t*) of a real tool t* along exactly one axis that leaves the tool superficially plausible but wrong for the task, with a deterministic failure semantics — calling c is unambiguous evidence of insensitivity to that property. The six-type taxonomy enumerates one canary per axis of the tool's contract: semantic decoy (paraphrased name, stale-data description), parameter trap (renamed params, unsatisfiable required api_key), capability mirage (inflated 'research-grade' claims), prerequisite blindness (omits required authentication), temporal decoy (outdated date/version), and granularity trap (over-specific hardcoded variant). Four types are generated by deterministic schema transforms; semantic and capability canaries use an LLM to reword names and descriptions. The framework also includes a provider-independent outcome judge and a trap detector that classifies each call as real, canary (with type), or unknown, producing per-task canary susceptibility rate (CSR) and type-specific trap rates.

What would settle it

Run the eight models on the same 120 tasks with the canary pool reworded by a different LLM and with the real tools' descriptions reworded in the same register; if frontier CSR rises significantly above the reported 0.075, the de-telling result does not generalize and the probes are partially measuring wording rather than reasoning.

Watch

Extended reading notes

Core claim

The central claim is that a canary — a deterministic perturbation of a real tool along exactly one dimension of its contract (name, description, parameter schema, or scope) — gives a wrong tool call unambiguous, typed failure semantics: observing the agent call the canary shows its selection was insensitive to the perturbed property. The paper builds a six-type taxonomy on this move (semantic decoy, parameter trap, capability mirage, prerequisite blindness, temporal decoy, granularity trap) and shows empirically that the taxonomy is capability-stratified: capability mirages catch mid-tier and weaker frontier models, while the other five probes are near-inert on strong hosted models but fire readily on small open-weight models. Two further findings are that nominal capability tier does not order canary resistance — the most susceptible hosted model is mid-tier and one mid-tier model beats its frontier sibling in the same provider — and that per-task canary susceptibility correlates negatively with task success (Spearman rho = -0.34). The de-telling ablation, which softens the give-away phrase of each probe, leaves frontier CSR essentially unchanged, which the authors take as evidence the probes measure reasoning rather than phrase-spotting.

Load-bearing premise

The method assumes that a canary call is caused by the specific reasoning weakness the canary targets, and not by generic attraction to added tools, tool ordering, formatting, or accidental wording differences introduced when canaries are reworded.

Editorial extensions

If this is right

Layering a small panel of typed canaries onto an existing tool-use suite upgrades its binary pass/fail into a reason code, giving developers a low-cost pre-deployment readiness check.
High capability-mirage susceptibility points to specific hardening: guardrails on 'more-powerful' tool variants and description-hygiene rules that avoid superlative capability claims in tool descriptions.
Recovery is a separate, capability-aligned axis: a trapped run that recovers succeeds about half the time, versus about one in six if it stays on the canary, so an explicit verify-and-backtrack step can turn a trap into a near-miss.
Because capability tier does not predict safety, tool-selection safety should be probed directly on each deployment rather than read off general model tier.
Per-task CSR predicts task failure (rho = -0.34), so canary susceptibility is a diagnostic signal for real degradation, not a benchmark artifact.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same perturbation logic could be extended beyond tool selection to other agent reasoning axes — planning, memory retrieval, or subgoal decomposition — where a binary failure could be given typed semantics.
The finding that raw canary density does not increase trap rates on hosted models (and often lowers them) suggests an attention-budget effect: with more competing tools, models read descriptions more carefully; this could be tested directly by varying the number of irrelevant tools while holding the canary constant.
The de-telling ablation does not fully separate a canary's tell from its lure, as the paper notes; a sharper test would reword the real tools in the same style as the softened canaries to rule out style-matching as the discriminator.
The provider-level pattern — the two models from one provider are the most resistant regardless of tier — hints that training-time emphasis on tool-description reading, rather than raw capability, drives canary resistance; a controlled fine-tuning experiment on a small open model could test this.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read it. The core idea is good: plant a perturbed tool that fails along one known axis, and a wrong pick becomes a reason code instead of a one-bit error. Applying misconception-probe design to tool selection is new as far as I know, and the six-type taxonomy is simple enough to be usable. Four of the six types are deterministic schema transforms, which gives clean failure semantics. The empirical work is careful: provider-independent judge with a second-judge check and a small human hand-grading, cluster-robust statistics, a controlled subtlety ablation, and 8,640 runs. The limits section is honest. The capability-mirage finding—that inflated capability claims catch even frontier models while other probes stay inert until small open models—is practically relevant and credible.

That said, the central attribution claim is oversold. For semantic and capability canaries, the generator rewrites both name and description, so a call could reflect surface attraction or novelty rather than the targeted weakness. The de-telling ablation softens the tell but cannot separate tell from lure, and the paper concedes exactly that. Because capability mirage is the one probe that fires on hosted models, this confound is load-bearing for the taxonomy's capability-stratified claim. I think the phenomenon is real—baseline CSR is zero, and the full-condition dilution argues against a generic more-tools effect—but the phrase "shows the agent's selection was insensitive to the perturbed property" in §3 is too strong for the LLM-rewritten types.

Two other issues. First, the CSR–TSR correlation (ρ=-0.34) is partly circular: a canary call usually causes task failure by design, so this validates the trap but not the diagnosis. The paper handles this better than most, but I would read it as a sanity check, not independent validation. Second, the abstract says "near-zero frontier rates," but Table 1 shows GPT-5.2 at 0.178, which is not near-zero. That overstatement should be fixed. Also, the paper claims "we release the framework" but gives no artifact link; given the reproducibility emphasis, that is a concrete omission.

All told: the method is new, the evaluation is solid, and the limitations are mostly the usual empirical ones. The attribution ambiguity is real but not fatal; it needs a sharper experimental separation, not a redesign. I would send this to a serious referee, and I would cite it if I were working on agent tool-selection evals.

Referee Report

2 major / 3 minor

Summary. The paper introduces "canary tools," diagnostic probe tools planted in an MCP tool set, each engineered to test one of six tool-selection weaknesses: semantic decoys, parameter traps, capability mirages, prerequisite blindness, temporal decoys, and granularity traps. The authors argue that observing a canary call yields a typed diagnosis rather than a one-bit "wrong tool" outcome. They evaluate eight models (six hosted, two 8B open-weight) on 120 tasks under baseline, declared, and full canary-density conditions with three seeds each, plus a 2,880-run subtlety ablation, using a provider-independent judge validated against a second judge and a human sample. The main findings are that canary susceptibility spans roughly 36x across models, that capability tier does not order safety, and that the taxonomy is capability-stratified, with capability mirages being the only probe active on strong hosted models. The framework, schemas, tasks, and logs are released.

Significance. If the central attribution claim holds, this is a genuinely useful diagnostic methodology: it converts a one-bit failure signal into a multi-dimensional profile, and it is unusually carefully evaluated. The empirical apparatus is a strength: a provider-independent judge with a second-judge check (Cohen's kappa = 0.75) and a human check (kappa = 0.90), cluster-robust statistics, deterministic canary generation, and explicit limitations. However, the diagnostic inference for the two empirically most important canary types (semantic and capability) is not directly validated: these canaries differ from the real tool in both name and description, so a call may reflect surface attraction rather than the targeted reasoning weakness. In addition, the CSR-TSR correlation is partly mechanical. The contribution is therefore stronger as a carefully executed measurement framework and a descriptive cross-model finding than as a fully established causal diagnosis of specific reasoning failures.

major comments (2)

[§3, §4, Appendix C, §6.7] The central inference in §3 — "observing a(q,T∪{c})=c shows the agent's selection was insensitive to the perturbed property" — is valid only if c differs from the real tool t* along the targeted axis alone. This holds for the deterministic schema transforms (parameter, prerequisite, temporal, granularity). For semantic and capability canaries, Appendix C states that an LLM rewrites both the name and the description, so a call to c could be driven by the reworded surface form, the name change, or generic novelty/positional attraction to an added tool, rather than by the specific weakness. This is load-bearing because capability mirage is the only strongly active probe on hosted models (Table 2, hosted average 0.216 versus at most 0.094 for the other types), so the headline empirical claim rests on an unconfoundedness assumption that is not established. The §6.7 de-telling ablation changes only the description phrasing and keeps names and ids fixed, so it does not address name-based or surface-form confounds; the paper itself concedes in the Limitations that the ablation "does not fully separate a canary's tell from its lure." I would suggest adding controlled variants (e.g., name-only rewording, description-only rewording, and a canary with an inflated capability claim but a neutral name) and/or independently validating the diagnosis, for example by coding the trace immediately before a canary call or by comparing canary calls against matched non-canary decoys.
[§6.4] The validation that "canaries predict failure" relies on the Spearman rho = -0.34 correlation between per-task CSR and TSR. This correlation is partly circular: a canary is constructed to be wrong for the task (stale data, auth errors, unsatisfiable parameters, or inflated but unreliable output), so a run that calls a canary is, by design, more likely to fail. The correlation is therefore a consistency check that the canaries have the intended effect rather than independent evidence that susceptibility reveals a distinct reasoning weakness. The baseline-versus-declared degradation analysis (Figure 7) is more informative but still conflates trap rate with outcome because a trap counts even when the agent recovers. I recommend framing the correlation explicitly as a sanity check and, if possible, reporting a version that controls for the raw number or timing of canary calls, or that uses recovery-adjusted outcomes.

minor comments (3)

[§4] The definition of per-task CSR as "mean over tasks of (canary calls / tool calls)" does not specify how tasks with zero tool calls are handled; Llama 3.1 8B in the full condition appears to make very few calls, and this should be stated explicitly in the metric definition.
[Appendix D] The human validation of the judge uses a sample of 40 task-runs graded by an author; please state whether the human was blind to model identity and canary condition, and report whether the two disagreements shown are the only cases of judge strictness or merely representative examples.
[Figures 5 and 6] The radial diagnostic profiles would benefit from a shared legend and explicit axis labels; as printed, it is difficult to compare the six spokes across the small multiples.

Circularity Check

1 steps flagged · score 3.0 of 10

One partially circular validation step: the CSR–TSR correlation follows in part from the definition of a canary as a tool that is wrong for the task; the central diagnostic methodology is otherwise self-contained.

self definitional [§6.4, building on §3]
"Per-task CSR correlates negatively with task success (Spearman ρ=−0.34, p<0.001, n=2880), validating canary susceptibility as a diagnostic for real degradation."

CSR counts calls to canaries, and §3 defines a canary as 'a targeted perturbation of a real tool t* ... that leaves the tool superficially plausible but wrong for q.' A canary call is therefore, by construction, a selection of a tool that is wrong for the task, which makes task failure more likely. The reported negative correlation is thus not an independent confirmation that susceptibility measures degradation; it is partly a restatement of the definition of a canary. The validation retains some empirical content because TSR is an outcome-based judge verdict and recovery can still save a trapped run, but the claim that this 'validates' the diagnostic overstates what the correlation can show.

full rationale

The paper is an empirical methodology study, not a derivation, and most of its claims are not circular. The six-type taxonomy is defined before measurement, the canary pool is generated once and persisted, baseline CSR=0 confirms that canaries are the only source of trapped calls, and the de-telling ablation is a genuine falsification test of phrase-spotting. There are no load-bearing self-citations: the references are standard external benchmarks and infrastructure, and no uniqueness theorem or prior result by the same authors is invoked to force the taxonomy. The one circular step is the validation correlation in §6.4: because §3 defines a canary as a tool that is 'wrong for q,' calling a canary is already a wrong selection by definition, so CSR–TSR correlation is partly tautological. This is a real but partial circularity, because recovery behavior and the outcome judge provide some independence. The paper's own limitation that the subtlety ablation 'does not fully separate a canary's tell from its lure' is a validity concern about typed attribution for capability mirages, not a circularity, and it does not raise the score further. Overall, the central diagnostic framework and the headline empirical findings stand independently; the score of 3 reflects the one partially definitional validation claim rather than a collapse of the paper's derivation.

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The ledger is light because this is an empirical benchmark, not a theory paper. The main hand-chosen inputs are the canary phrase cues, task difficulty labels, and the taxonomy itself. The key domain assumption is that a canary call is diagnostic of a specific reasoning weakness, which is plausible but not directly proven.

free parameters (3)

Give-away suffix phrases for phrase-based canaries = e.g., 'returns cached data', 'research-grade', 'ADVANCED: solves the hardest cases'
These phrases are chosen by the authors and are the only cue that distinguishes several canary types from real tools; the subtlety ablation replaces them with different hand-chosen phrases.
Softened phrases in subtlety ablation = e.g., 'optimized variant with extended precision'
Hand-selected by authors to remove the obvious tell while keeping the lure; this choice affects the ablation result.
Task difficulty labels (easy/medium/hard) = 40 tasks per tier
Difficulty is assigned by author judgment, with no objective calibration; the paper's finding that model identity dominates difficulty depends on this assignment.

assumptions (4)

domain assumption A canary call unambiguously indicates the targeted reasoning weakness; insensitivity to the perturbed property is the only cause.
In §3, the paper defines canaries so that observing a call shows the agent was insensitive to the perturbed property. This assumes no other factor, such as ordering, formatting, or generic attraction, causes the call.
domain assumption Realistic synthetic tool outputs do not distort model behavior after the fix.
§4 notes early placeholder outputs made strong models stop; the paper assumes realistic synthetic data restores equal footing. The assumption is plausible but unproven.
domain assumption The provider-independent judge (DeepSeek-V3.2 at temperature 0) grades task success accurately.
§4 and Appendix D validate with a second judge and a 40-run human check; the assumption is supported but the human check is not fully blind.
ad hoc to paper Default decoding settings of each provider are appropriate for the comparison.
§5 says agents are queried with default decoding settings; this choice could affect behavior across providers and is not controlled.

invented entities (2)

Canary tools
purpose: Diagnostic probe tools planted in an agent's MCP tool set; each engineered to expose one tool-selection weakness.
A new evaluation artifact introduced by this paper; it makes no predictions outside the study and is not independently falsifiable.
Six-type canary taxonomy
purpose: A categorization of tool-selection failures: semantic decoy, parameter trap, capability mirage, prerequisite blindness, temporal decoy, granularity trap.
A conceptual framework introduced to structure the diagnostics; its usefulness is demonstrated only within this paper's data.

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Cite this review

Pith. "Pith review of Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools." pith.science (2026). https://pith.science/paper/LQN7OZYF

@misc{pith2026260804719,
  author       = {Pith},
  title        = {Pith review of: Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LQN7OZYF}},
  note         = {Machine review of arXiv:2608.04719}
}

read the original abstract

Agent evaluations tell us that a model picked the wrong tool, but rarely why. We introduce canary tools: diagnostic probe tools planted in an agent's Model Context Protocol (MCP) tool set, each engineered to probe one specific tool-selection weakness. A six-type taxonomy (semantic decoys, parameter traps, capability mirages, prerequisite blindness, temporal decoys, and granularity traps) turns a single "wrong tool" outcome into a multi-dimensional profile of how a model reasons about tools. We evaluate eight models -- six hosted and two 8B open-weight -- spanning three capability tiers, on 120 tasks across three canary-density conditions and three seeds (8,640 runs), plus a 2,880-run subtlety ablation. Task success is graded by a provider-independent judge, corroborated by a second independent judge (Cohen's kappa = 0.75). We report three findings. First, susceptibility drops sharply as models get more capable: the per-task canary susceptibility rate (CSR) ranges about 36x across models, lowest for Claude Opus 4.8 and highest for Llama 3.1 8B. Second, capability tier alone does not predict safety: the most susceptible hosted model is mid-tier, and within a provider the cheaper model can be the safer one. Third, the taxonomy is capability-stratified: capability mirages most reliably trap frontier models, while the other types are largely inert on strong models but fire on small open models, so they discriminate by capability rather than being weak. Softening each canary's give-away phrase leaves frontier CSR essentially unchanged, evidence that the probes measure reasoning, not phrase-spotting. Susceptibility also predicts task failure (Spearman rho = -0.34), while the most robust models are not significantly degraded by canary pressure. We release the framework, canary schemas, tasks, and logs.

Figures

Figures reproduced from arXiv: 2608.04719 by the authors.

**Figure 2.** Per-task canary susceptibility rate per model [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** A canary probe in action (real run). The same [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Per-task CSR by task difficulty (declared). Model identity, not difficulty, dominates: most susceptible models peak on medium tasks, and Llama 3.1 8B traps less on hard tasks (where it makes fewer calls), while Qwen3-8B spikes there. Kruskal-Wallis test on per-task CS…

**Figure 5.** Figure 5: Type-specific trap rate, models × canary types (declared). The diagnostic centerpiece: capability mirage is the only column active across strong models; the small model lights up the full taxonomy [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: Per-model diagnostic profiles (small multiples; [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 8.** Figure 8: Subtlety ablation (declared): per-task CSR on standard vs. softened (subtle) canaries. Softening the give-away phrase does not raise the trap rate of the strong models (frontier average 0.079→0.075), so their low rates are not an artifact of phrase-spotting. family doe…

**Figure 9.** Figure 9: Task outcome for trapped runs, split by whether the agent recovered. Recovering from a trap lifts task success from 16% to 52%. are not equally safe if one reliably backs out and the other does not. 10 [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

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Reference graph

Works this paper leans on

27 extracted references · 16 canonical work pages

[1]

Anthropic. 2024. Introducing the model context protocol. https://www.anthropic.com/news/model-context-protocol

work page 2024
[2]

Chaithanya Bandi, Razvan-Gabriel Dumitru, Ben Hertzberg, Divyansh Agarwal, et al. 2026. MCP-Atlas : A large-scale benchmark for tool-use competency with real MCP servers. arXiv preprint arXiv:2602.00933

arXiv 2026
[3]

Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, and Lichao Sun. 2024. MetaTool benchmark for large language models: Deciding whether to use tools and which to use. In International Conference on Learning Representations (ICLR)

work page 2024
[4]

Wenrui Liu, Zixiang Liu, Elsie Dai, Wenhan Yu, Lei Yu, Tong Yang, Jinjun Han, and Hong Gao. 2025. MCPAgentBench : A real-world task benchmark for evaluating LLM agent MCP tool use. arXiv preprint arXiv:2512.24565

arXiv 2025
[5]

Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, et al. 2024. AgentBench : Evaluating LLMs as agents. In International Conference on Learning Representations (ICLR)

work page 2024
[6]

Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, and Ruoming Pang. 2024. ToolSandbox : A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities. arXiv preprint arXiv:2408.04682

arXiv 2024
[7]

Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E

Shishir G. Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E. Gonzalez. 2025. The Berkeley function calling leaderboard ( BFCL ): From tool use to agentic evaluation of large language models. In Proceedings of the 42nd International Conference on Machine Learning (ICML)

work page 2025
[8]

Patil, Tianjun Zhang, Xin Wang, and Joseph E

Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. 2024. Gorilla: Large language model connected with massive APIs . In Advances in Neural Information Processing Systems (NeurIPS)

work page 2024

Show all 27 references

[9]

Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, et al. 2024. ToolLLM : Facilitating large language models to master 16000+ real-world APIs . In International Conference on Learning Representations (ICLR)

2024
[10]

Timo Schick, Jane Dwivedi-Yu, Roberto Dess \`i , Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. In Advances in Neural Information Processing Systems (NeurIPS)

2023
[12]

Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. ReAct : Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR)

2023
[13]

Qiusi Zhan, Zhixiang Liang, Zifan Ying, and Daniel Kang. 2024. InjecAgent : Benchmarking indirect prompt injections in tool-integrated large language model agents. In Findings of the Association for Computational Linguistics: ACL 2024

2024
[14]

Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, and Hayato Yamana. 2024. ToolBeHonest : A multi-level hallucination diagnostic benchmark for tool-augmented large language models. ...

2024
[15]

Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan , booktitle=
[16]

Advances in Neural Information Processing Systems (NeurIPS) , year=

Toolformer: Language Models Can Teach Themselves to Use Tools , author=. Advances in Neural Information Processing Systems (NeurIPS) , year=
[17]

and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E

Patil, Shishir G. and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E. , booktitle=. Gorilla: Large Language Model Connected with Massive
[18]

Qin, Yujia and Liang, Shihao and Ye, Yining and Zhu, Kunlun and Yan, Lan and Lu, Yaxi and others , booktitle=
[19]

Liu, Xiao and Yu, Hao and Zhang, Hanchen and Xu, Yifan and Lei, Xuanyu and Lai, Hanyu and others , booktitle=
[20]

2024 , howpublished=

Introducing the Model Context Protocol , author=. 2024 , howpublished=

2024
[21]

Huang, Yue and Shi, Jiawen and Li, Yuan and Fan, Chenrui and Wu, Siyuan and Zhang, Qihui and Liu, Yixin and Zhou, Pan and Wan, Yao and Gong, Neil Zhenqiang and Sun, Lichao , booktitle=
[22]

arXiv preprint arXiv:2406.12045 , year=

-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains , author=. arXiv preprint arXiv:2406.12045 , year=

arXiv
[23]

Zhang, Yuxiang and Chen, Jing and Wang, Junjie and Liu, Yaxin and Yang, Cheng and Shi, Chufan and Zhu, Xinyu and Lin, Zihao and Wan, Hanwen and Yang, Yujiu and Sakai, Tetsuya and Feng, Tian and Yamana, Hayato , booktitle=
[24]

Zhan, Qiusi and Liang, Zhixiang and Ying, Zifan and Kang, Daniel , booktitle=
[25]

Liu, Wenrui and Liu, Zixiang and Dai, Elsie and Yu, Wenhan and Yu, Lei and Yang, Tong and Han, Jinjun and Gao, Hong , journal=
[26]

and Mao, Huanzhi and Yan, Fanjia and Ji, Charlie Cheng-Jie and Suresh, Vishnu and Stoica, Ion and Gonzalez, Joseph E

Patil, Shishir G. and Mao, Huanzhi and Yan, Fanjia and Ji, Charlie Cheng-Jie and Suresh, Vishnu and Stoica, Ion and Gonzalez, Joseph E. , booktitle=. The
[27]

Lu, Jiarui and Holleis, Thomas and Zhang, Yizhe and Aumayer, Bernhard and Nan, Feng and Bai, Felix and Ma, Shuang and Ma, Shen and Li, Mengyu and Yin, Guoli and Wang, Zirui and Pang, Ruoming , journal=
[28]

Bandi, Chaithanya and Dumitru, Razvan-Gabriel and Hertzberg, Ben and Agarwal, Divyansh and others , journal=

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Anthropic. 2024. Introducing the model context protocol. https://www.anthropic.com/news/model-context-protocol

work page 2024

[2] [2]

Chaithanya Bandi, Razvan-Gabriel Dumitru, Ben Hertzberg, Divyansh Agarwal, et al. 2026. MCP-Atlas : A large-scale benchmark for tool-use competency with real MCP servers. arXiv preprint arXiv:2602.00933

arXiv 2026

[3] [3]

Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, and Lichao Sun. 2024. MetaTool benchmark for large language models: Deciding whether to use tools and which to use. In International Conference on Learning Representations (ICLR)

work page 2024

[4] [4]

Wenrui Liu, Zixiang Liu, Elsie Dai, Wenhan Yu, Lei Yu, Tong Yang, Jinjun Han, and Hong Gao. 2025. MCPAgentBench : A real-world task benchmark for evaluating LLM agent MCP tool use. arXiv preprint arXiv:2512.24565

arXiv 2025

[5] [5]

Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, et al. 2024. AgentBench : Evaluating LLMs as agents. In International Conference on Learning Representations (ICLR)

work page 2024

[6] [6]

Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, and Ruoming Pang. 2024. ToolSandbox : A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities. arXiv preprint arXiv:2408.04682

arXiv 2024

[7] [7]

Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E

Shishir G. Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E. Gonzalez. 2025. The Berkeley function calling leaderboard ( BFCL ): From tool use to agentic evaluation of large language models. In Proceedings of the 42nd International Conference on Machine Learning (ICML)

work page 2025

[8] [8]

Patil, Tianjun Zhang, Xin Wang, and Joseph E

Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez. 2024. Gorilla: Large language model connected with massive APIs . In Advances in Neural Information Processing Systems (NeurIPS)

work page 2024