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REVIEW 3 major objections 4 minor 41 cited by

This paper introduces MCP-Bench, a benchmark of 104 multi-step tasks over 28 live MCP servers and 250 tools, and claims that even frontier LLM agents score below 0.75 overall, with long-horizon planning as the main bottleneck.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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MCP-Bench introduces a 104-task, 250-tool MCP-based benchmark where even the strongest LLM scores 0.749, with planning and multi-server orchestration as the key weaknesses.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection MCP-Bench is a real scale-up for MCP tool-use evaluation, but the headline numbers aren't reproducible from the method as written, and the judge's low-score default makes the absolute 'far from proficient' claim hard to read. the 3 major comments →

arxiv 2508.20453 v1 pith:4TSBJ2Q3 submitted 2025-08-28 cs.CL

MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Zhenting Wang , Qi Chang , Hemani Patel , Shashank Biju , Cheng-En Wu , Quan Liu , Aolin Ding , Alireza Rezazadeh
show 3 more authors
Ankit Shah Yujia Bao Eugene Siow
This is my paper
classification cs.CL
keywords tool-using LLM agentsModel Context Protocolbenchmarkmulti-step planningLLM-as-a-judgetool retrievalcross-server orchestrationagentic reasoning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

This paper introduces MCP-Bench, a benchmark that connects LLM agents to 28 live MCP servers exposing 250 tools across finance, travel, science, academic search, and other domains. It generates 104 multi-step, fuzzy natural-language tasks that force agents to discover tools without being told their names, chain outputs across servers, ground answers in intermediate data, and coordinate several goals at once. The central claim is that current LLM agents are far from proficient at this kind of realistic tool use: the best overall score is 0.749, while schema compliance and basic execution are near-converged above 95% for many models. The headline distinction is planning: dependency awareness and parallelism separate the strongest models from weaker ones much more sharply than any execution metric. If MCP-Bench is right, the bottleneck in tool-using agents has moved from reading tool schemas to orchestrating them.

Core claim

The central discovery MCP-Bench is built to expose is that modern LLM agents handle the mechanics of tool use well and the orchestration poorly. Across 20 models, valid tool name rates are mostly above 95% and strong models exceed 98% schema compliance, but overall task scores top out at 0.749, and the largest model-to-model gaps appear in dependency awareness and parallelism, not execution fidelity. The paper interprets this as evidence that basic tool execution has largely converged while long-horizon planning, cross-server coordination, and evidence grounding remain the differentiators. The claim is not that agents cannot call tools; it is that they cannot reliably chain many complementar

What carries the argument

The load-bearing machinery is the combination of a genuine MCP server ecosystem and a two-tier evaluation. Each task runs against 28 production MCP servers, and 10 distractor servers are attached per instance so tool retrieval happens in a large heterogeneous space. An LLM-guided task synthesis pipeline first discovers dependency chains from tool input/output signatures, filters tasks for solvability and utility, then rewrites them into fuzzy conversational instructions that hide tool names and execution steps. Evaluation combines rule-based checks for tool name validity, schema compliance, runtime success, and dependency order with an LLM-as-a-judge rubric that scores task completion, tool

Load-bearing premise

The rankings stand on the o4-mini judge's rubric scores being accurate measures of completion, grounding, and planning across the 104 tasks, but the judge was validated only on a separate 50-task set and belongs to the same model family that generated the tasks.

What would settle it

Independently re-score a random sample of raw execution traces from the 104 tasks, either with human annotators using the paper's own rubrics or with a judge from a different model family, and compare the resulting model ordering to o4-mini's. Also check that each task admits a reference trajectory using only tools in its allowed set. If the ordering flips or many tasks lack a solvable trajectory, the central difficulty claim fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If planning is the main differentiator, progress on tool-using agents should come from dependency-aware planning and long-horizon coordination rather than better schema parsing.
  • Multi-server settings expose a robustness gap: weaker models degrade noticeably in dependency awareness and parallelism, while strong models stay stable, making cross-server orchestration a distinct capability to measure.
  • Fuzzy task descriptions that hide tool names can be reused by other MCP-based benchmarks to test tool discovery and retrieval under underspecification.
  • Prompt shuffling and score averaging reduce judge variance, with coefficient of variation dropping from 16.8% to 15.1%, suggesting the evaluation pipeline is more stable despite relying on an LLM judge.
  • The top overall score still below 0.75 indicates that no tested model comes close to mastering complex, multi-server tool use, leaving clear headroom for future agent designs.

Where Pith is reading between the lines

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

  • A natural next test would be to re-score the 104 tasks with a judge from a different model family or with human gold labels, since o4-mini both synthesized the tasks and judged them, so rankings could partly reflect judge-model affinity.
  • Because MCP-Bench runs on live servers, tool outputs change over time; freezing a snapshot of tool responses would make leaderboards reproducible across later runs.
  • The paper's tool-call efficiency data suggest a Pareto frontier metric combining task score with number of rounds or calls could make efficiency a first-class evaluation axis.
  • The fuzzy-instruction format could connect directly to tool-retrieval ranking problems, where the task is to select the right tools from a large action space before any execution happens.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. MCP-Bench introduces a benchmark for LLM agents that use Model Context Protocol (MCP) servers. It comprises 104 tasks built from 28 real MCP servers and 250 tools, with tasks synthesized by an LLM pipeline that first discovers tool dependency chains, filters for solvability/utility, and rewrites instructions into fuzzy, natural-language requests. Agents are evaluated with a two-tier system: rule-based metrics for tool-name validity, schema compliance, execution success, and dependency order; and an LLM-as-a-judge rubric for task completion, tool usage, and planning effectiveness, with prompt shuffling and score averaging. Experiments over 20 LLMs report high schema compliance and execution success but substantially lower planning and task-completion scores, leading the authors to conclude that planning and reasoning are the main differentiators among current agents.

Significance. If the evaluation is trustworthy, MCP-Bench is a useful, more realistic complement to existing tool-use benchmarks: it covers multiple coordinated servers, fuzzy instructions, information grounding, and cross-domain orchestration, and it ships with code and data. The rule-based metrics (valid tool, schema compliance, execution success) are objective and reproducible, and the scale of the model comparison (20 models) is a strength. However, the headline leaderboard and the central 'planning is the key differentiator' claim rest on the LLM-judge scores, and the manuscript's own validation of that judge is performed on a different, simpler task set. Given that the judge prompt also contains explicit low-score calibration anchors, the absolute proficiency numbers and some comparative conclusions are currently fragile. The paper is worth publishing after the judge is validated on the actual benchmark and after the score aggregation is made transparent and reproducible.

major comments (3)
  1. [§5.2, Table 3] The reported Overall Scores are not reproducible from the aggregation described in §5.2. The text says sub-dimensions are averaged within each axis and the final judgment score is the average of axis-level scores. For gpt-5, the six sub-scores in Table 3 are 0.677, 0.828, 0.767, 0.749, 0.649, 0.339; both the average of the six sub-scores and the average of the three implied axis means are 0.668, not the reported 0.749. For o3 the corresponding average is 0.625, not 0.715. Since Overall Score is the headline leaderboard quantity used for the paper's main conclusions, the exact formula must be stated and the tables/text corrected, or the discrepancy resolved.
  2. [§6.4 and Appendix A.4] The LLM judge is validated only on a separate 50-task WebSearch/Time set, not on the 104 MCP-Bench tasks. The reported human agreement of 1.43/2 is moderate, and the ablation shows only a modest CV reduction (16.8% to 15.1%). Meanwhile, the judge prompt in Appendix A.4 contains explicit low-score anchors: 'Default to 4–5 unless you have strong evidence for higher', 'Most real-world executions should score 4–6', and 'Scores of 8+ should be EXCEPTIONAL'. These instructions compress the score scale, so the absolute statement that even gpt-5 (0.749) is 'far from proficient' is at least partly a property of the rubric rather than a measured property of the agent. The authors should validate the judge on MCP-Bench tasks (e.g., with human annotations on a representative sample), report per-axis agreement and score distributions, and show that the main rankings and the planning-differentiator co
  3. [§4.2, §5.2, Appendix A.4] The same model family (o4-mini) is used both for task synthesis and as the default judge, and the judge is given the dependency analysis produced during synthesis. This is not derivation-level circularity, but it creates a shared-bias risk: if o4-mini's dependency analysis is inaccurate or favors a particular reasoning style, the judge may systematically reward models that happen to follow that style. The paper should provide a robustness check with at least one independent judge family (e.g., a Claude or Gemini model) on a subset of tasks, and report agreement across judges and across model families. It would also strengthen the benchmark to report how many of the 104 tasks were independently human-verified for solvability, rather than relying only on the LLM-based quality filter.
minor comments (4)
  1. [§4.2] Typo: 'disgarded' should be 'discarded'.
  2. [§6.4] The text says 'As shown in Table 1' for the ablation results; the correct reference is Table 7.
  3. [§2 / References] The benchmark name is spelled inconsistently as 'MCP-RADER' in the introduction and 'MCP-RADAR' in the related-work section; please unify.
  4. [§5.2 / Table 3] The table header 'Schema Understanding' combines valid-tool name and schema compliance; the relationship between these rule-based columns and the LLM-judge axes should be clarified in the caption, especially since the 'Overall Score' appears to be a different aggregation than the six listed sub-scores.

Circularity Check

0 steps flagged

No circularity: benchmark scores are empirical measurements, not fitted inputs or self-citation-derived claims.

full rationale

MCP-Bench is an empirical benchmark rather than a derived prediction chain. The leaderboard and capability conclusions come from rule-based trajectory metrics plus LLM-as-a-judge rubric scores; no parameter is fitted to a subset of data and then reported as a prediction, and no result is defined in terms of the claim it is supposed to establish. The judge is given the dependency analysis and concrete task reference produced during synthesis, but this serves as a gold-standard evaluation specification, not as an input that forces the reported scores. Rule-based metrics (valid tool name rate, schema compliance, execution success) independently support the claim that basic execution has converged, while the planning-differentiator conclusion relies on judge sub-scores that are not statistically forced by the rubric's calibration anchors ('Default to 4–5', 'Most real-world executions should score 4–6'); those anchors affect absolute levels but do not determine the comparative ordering across 20 models. No load-bearing self-citation, imported uniqueness theorem, or ansatz-smuggled-by-citation appears. The apparent inconsistency between the Overall Score aggregation described in Section 5.2 and the reported values in Table 3 is a reproducibility defect, not circularity. The use of o4-mini for both task synthesis and judging raises validity concerns, but this is not a case where the result is equivalent to its input by construction, so it does not change the circularity verdict.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

MCP-Bench introduces no physical or mathematical entities. Its central claims rest on hand-chosen quality and budget thresholds, on the unvalidated assumption that o4-mini's self-assessed solvability and judge scores are reliable, and on the stability of live, third-party MCP servers.

free parameters (5)
  • solvability threshold = 9.0/10
    Chosen by hand in Section 4.2; tasks below this LLM-assigned score are discarded, directly determining the benchmark composition.
  • utility threshold = 5.0/10
    Chosen by hand in Section 4.2; controls which generated tasks are judged practically useful.
  • max execution rounds T_max = 20
    Set in Algorithm 1 and Section 3.1; caps agent budget and defines what counts as task failure by exhaustion.
  • number of judge shuffles = 5
    Chosen in Section 5.2; more shuffles would change score stability and cost.
  • distractor servers per task = 10
    Section 3.2: 10 distractor servers add 100+ tools per task, a hand-selected difficulty knob.
axioms (4)
  • domain assumption o4-mini-generated dependency analyses and tasks labeled solvable (>=9/10) are genuinely solvable and realistic.
    Section 4.2 relies on the synthesis LLM's self-assessed solvability, with only unspecified human inspection, to admit tasks into the benchmark.
  • domain assumption LLM-as-a-judge rubric scores are valid measures of task completion, grounding, tool appropriateness, and planning quality.
    Section 5.2 assumes judge scores reflect agent capability; validation in Section 6.4 is on a separate 50-task WebSearch/Time set, not on the 104 MCP-Bench tasks.
  • domain assumption Live MCP servers return correct and sufficiently stable data across evaluation runs.
    Sections 3.1 and 4.1 run agents against production servers; real-world data drift and rate limits are not controlled, which affects both solvability and judge scoring.
  • ad hoc to paper The dependency analysis given to the judge is correct and is the right reference for scoring.
    The judge prompt (Section A.4) feeds the synthesis-time dependency analysis to the judge as the reference, so judge scores inherit any errors in that analysis.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers." pith.science (2026). https://pith.science/paper/4TSBJ2Q3

@misc{pith2026250820453,
  author       = {Pith},
  title        = {Pith review of: MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4TSBJ2Q3}},
  note         = {Machine review of arXiv:2508.20453}
}
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read the original abstract

We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28 representative live MCP servers spanning 250 tools across domains such as finance, traveling, scientific computing, and academic search. Unlike prior API-based benchmarks, each MCP server provides a set of complementary tools designed to work together, enabling the construction of authentic, multi-step tasks with rich input-output coupling. Tasks in MCP-Bench test agents' ability to retrieve relevant tools from fuzzy instructions without explicit tool names, plan multi-hop execution trajectories for complex objectives, ground responses in intermediate tool outputs, and orchestrate cross-domain workflows - capabilities not adequately evaluated by existing benchmarks that rely on explicit tool specifications, shallow few-step workflows, and isolated domain operations. We propose a multi-faceted evaluation framework covering tool-level schema understanding and usage, trajectory-level planning, and task completion. Experiments on 20 advanced LLMs reveal persistent challenges in MCP-Bench. Code and data: https://github.com/Accenture/mcp-bench.

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Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages · cited by 35 Pith papers

  1. [1]

    Assess if the original task is fully completed

    work page

  2. [2]

    If not complete, decide if another round would provide significant value

    work page

  3. [3]

    reasoning

    If continuing, plan PARALLEL tool executions for this round PARALLEL EXECUTION PLANNING (if continuing): • Plan ALL tool calls for this round to execute in PARALLEL • ALL tools in this round will run simultaneously without dependencies • EARLYEXECUTIONPRINCIPLE: Plan all necessary tool calls that don’t require dependencies • AVOID REDUNDANT CALLS:Don’t re...

    work page

  4. [4]

    ALL tasks MUST be self-contained and executableWITHOUT any external dependencies

    work page

  5. [5]

    https://api.example.com

    NEVER reference external resourceslike: • URLs (like "https://api.example.com" or any external API) • Local files (like "user-management.yaml" or "config.json") • Databases or external systems • "Our API", "our system", "our database"

    work page

  6. [6]

    ALL data must come from either: • The provided tools themselves (what they can generate/fetch/calculate) • Concrete values you specify in the task (numbers, names, parameters)

    work page

  7. [7]

    user-provided parameters

    NEVER use vague references: • "user-provided parameters" or "user-specified" • "fetched from database" or "retrieved from external source" • "based on user preferences" or "according to input" • "specified location/value" or "to be determined"

    work page

  8. [8]

    analyze heat exchanger with inlet temp 80°C, outlet 60°C, flow rate 0.5 kg/s

    ALWAYS provide concrete values: • Specific numbers (e.g., "analyze heat exchanger with inlet temp 80°C, outlet 60°C, flow rate 0.5 kg/s") • Named entities (e.g., "analyze weather in San Francisco" not "specified city") • For locations: Use city names, landmark names, or general areas, NOT specific street addresses – GOOD: "San Francisco", "Times Square", ...

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  9. [9]

    If the task involves analysis, provide ALL input data in the task description: • For calculations: provide all numbers, formulas, and units needed • For searches: provide specific search terms and criteria • For comparisons: provide specific items with their properties • For optimization: provide current values and target metrics Requirements:

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  10. [10]

    MUST require multiple tools in a specific sequence

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  11. [11]

    Tool B should need output from Tool A (dependency chain)

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  12. [12]

    Include decision points based on intermediate results

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  13. [13]

    Be realistic and valuable for business/research purposes

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  14. [14]

    Define expected analysis and output format

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  15. [15]

    Task must be immediately executable - agent should never need to ask for more information

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  16. [16]

    task_id":

    Task should be executable and solvable by using the provided tools. You need to pay attention to the function and the output of the provided tools. Output Format: Output ONLY a JSON object (not an array). ALWAYS USE relative dates/times: { 34 MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers "task_id": "task_XXX",...

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  17. [17]

    SOLVABILITY (1-10): • 10: All required data is provided, tools perfectly match needs, clear success criteria • 8-9: Task is clearly solvable with the given tools, minor ambiguities acceptable • 6-7: Mostly solvable but some steps may be challenging or unclear • 4-5: Significant gaps in tool coverage or data requirements • 1-3: Task cannot be meaningfully ...

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  18. [18]

    solvability_score

    UTILITY (1-10): • 10: Critical business/research value, addresses real-world problem perfectly • 8-9: Strong practical value, useful for decision-making or operations • 6-7: Moderate value, interesting but not critical • 4-5: Limited practical value, mostly academic exercise • 1-3: Trivial or artificial task with no real-world application Consider: • Does...

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  19. [19]

    • 4–6: Perfectly completes 40–60% of requirements

    Task Fulfillment • 1–3: Perfectly completes 10–30% of requirements. • 4–6: Perfectly completes 40–60% of requirements. • 7–8: Perfectly completes 70–80% of requirements. • 9–10: Perfectly completes 90–100% of requirements

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  20. [20]

    • 4–6: 40–60% of claims are perfectly grounded in tool outputs

    Grounding • 1–3: 10–30% of claims are perfectly grounded in tool outputs. • 4–6: 40–60% of claims are perfectly grounded in tool outputs. • 7–8: 70–80% of claims are perfectly grounded in tool outputs. • 9–10: 90–100% of claims are perfectly grounded in tool outputs. 38 MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Se...

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  21. [21]

    • 4–6: 40–60% of tools were perfectly selected for their subtasks

    Tool Appropriateness • 1–3: 10–30% of tools were perfectly selected for their subtasks. • 4–6: 40–60% of tools were perfectly selected for their subtasks. • 7–8: 70–80% of tools were perfectly selected for their subtasks. • 9–10: 90–100% of tools were perfectly selected for their subtasks

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  22. [22]

    • 4–6: 40–60% of tool calls have perfectly accurate and complete parameters

    Parameter Accuracy • 1–3: 10–30% of tool calls have perfectly accurate and complete parameters. • 4–6: 40–60% of tool calls have perfectly accurate and complete parameters. • 7–8: 70–80% of tool calls have perfectly accurate and complete parameters. • 9–10: 90–100% of tool calls have perfectly accurate and complete parameters. Planning Effectiveness and E...

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  23. [23]

    • 4–6: 40–60% of dependency chains are perfectly executed

    Dependency Awareness • 1–3: 10–30% of dependency chains are perfectly executed. • 4–6: 40–60% of dependency chains are perfectly executed. • 7–8: 70–80% of dependency chains are perfectly executed. • 9–10: 90–100% of dependency chains are perfectly executed

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  24. [24]

    • 4–6: 40–60% redundant calls OR 40–60% of parallelizable tasks were executed in parallel

    Parallelism and Efficiency • 1–3: More than 70% redundant calls OR less than 30% of parallelizable tasks were executed in parallel. • 4–6: 40–60% redundant calls OR 40–60% of parallelizable tasks were executed in parallel. • 7–8: 20–30% redundant calls AND 70–80% of parallelizable tasks were executed in parallel. • 9–10: Less than 10% redundant calls AND ...

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  25. [25]

    perfectly executed

    When evaluating percentages, be EXTREMELY STRICT about what counts as “perfectly executed”

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  26. [26]

    Perfectly

    “Perfectly” means ALL of the following must be true: • Correct tool selection (not just “works” but OPTIMAL choice) 39 MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers • Complete and accurate parameters (not just valid, but IDEAL) • Zero redundancy (no repeated or unnecessary calls) • Proper error handling (grace...

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  27. [27]

    If ANY of the above is missing, that portion is NOT perfectly executed (counts as 0%)

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  28. [28]

    Example: Task completed correctly but with 1 redundant call = that portion is 0% perfect KEY PRINCIPLES:

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  29. [29]

    ALWAYS calculate as percentage, NOT absolute numbers

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  30. [30]

    10 errors in 100 calls (10%) = same score as 1 error in 10 calls (10%)

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  31. [31]

    Consider the OPPORTUNITY COUNT for each dimension: • Tool calls: How many total calls were made? • Parallelization: How many tasks COULD have been parallel? • Parameters: How many total parameters across all calls? • Claims: How many factual statements were made? • Dependencies: How many dependency relationships exist?

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  32. [32]

    perfectly executed

    NORMALIZE by complexity - don’t punish complex tasks: • Simple task: 1 error/5 steps (20% defect) = Score 7 • Complex task: 4 errors/20 steps (20% defect) = Score 7 CRITICAL: Apply the STRICTEST interpretation of “perfectly executed”. If there’s ANY doubt, score lower. CONCRETE SCORING EXAMPLES WITH PROPORTIONS: Task Fulfillment: • Completed 19/20 require...

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  33. [33]

    You MUST map each score to the exact percentage ranges in the rubrics

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  34. [34]

    Task Completion and Tool Selection MUST be evaluated against the CONCRETE TASK REFERENCE, not the fuzzy task

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  35. [35]

    Planning Effectiveness should be evaluated based on the PROPORTION of dependencies correctly handled, not the absolute number of steps executed or exact conformance to the dependency analysis

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  36. [36]

    First calculate the actual percentage of completion/success, then assign the corresponding score range

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  37. [37]

    task_fulfillment_reasoning

    IMPORTANT: Focus on completion RATIOS not absolute numbers - completing 7/10 steps (70%) should score similarly to completing 14/20 steps (70%), regardless of task complexity. Please score based on COMPLETION PERCENTAGES and PROPORTIONAL SUCCESS, not absolutenumbersoftoolscalledorstepsexecuted. Returnyourevaluationscoringandreasoning in this exact JSON fo...

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This paper was first reviewed by deepseek-v4-flash on August 5, 2026.