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OpenArx

by ai.openarx

find_benchmark_results

Query structured benchmark scores from research papers. Returns leaderboard-style results: task, dataset, metric, score, method, paper, year. Backed by LLM-extracted benchmark records, filtered to performance metrics only (accuracy / F1 / BLEU / ROUGE / mAP / top-1 / top-5 / pass@k / etc.) — model-size, FLOPs, dataset cardinality and similar are excluded. Best for ML benchmark / leaderboard papers; may return empty for theoretical / survey papers without numerical results. Filter by task, dataset, metric. Use for SOTA tracking, SOTA-trajectory analysis, comparing methods on common benchmarks. At least one of task / dataset / metric is required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoTask name like "question answering", "image classification" (case-insensitive partial match). REQUIRED if dataset and metric are not provided.
topKNoTop-K results by score
detailNostandard
metricNoMetric name like "F1", "accuracy", "BLEU". REQUIRED if task and dataset are not provided.
run_idNoOptional. The active methodist run_id (as returned by the methodist diagnose / get_current_dose door). Pass it whenever you call this tool while working inside a run, so the call is attributed to that run for the §8 usage crosscheck — attribution is run-anchored, so it stays correct even if your access token refreshes mid-run. Must be YOUR run: a run_id owned by a different principal, or a non-existent run_id, is rejected.
datasetNoDataset name like "SQuAD", "ImageNet", "GLUE". REQUIRED if task and metric are not provided.
minYearNoYear >= (e.g. 2023 to filter out older results)
minScoreNoMinimum benchmark score (filter for SOTA leaderboard view)
categoriesNoarXiv category filter

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses that results are LLM-extracted, filtered to performance metrics, and that certain non-performance attributes like model-size are excluded. It also notes possible empty results for non-numerical papers. Does not mention authentication or rate limits, but these are not critical for this read-only tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is informative and well-structured, front-loading the main purpose and then providing details. A few sentences could be tightened, but overall it earns its length.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 9 parameters and no output schema, the description does an excellent job explaining what the tool returns, when it works, and required filtering conditions. It covers scope, limitations, and use cases comprehensively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 89%, so baseline is 3. The description adds context about the output format and filtering logic, which helps interpret parameters, but does not add substantial new meaning per parameter beyond the schema's own descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it queries structured benchmark scores from research papers, specifies the return fields (task, dataset, metric, score, method, paper, year), and distinguishes itself from general paper search by focusing on performance metrics. This is specific and distinct from sibling tools like search_keyword.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states it is 'Best for ML benchmark / leaderboard papers' and may return empty for theoretical/survey papers. Provides clear use cases: SOTA tracking, trajectory analysis, comparing methods. Also specifies the requirement that at least one of task/dataset/metric must be provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation4/5

Most tools have clear distinct purposes, but some overlap exists between explore_topic and methodist_explore_topic, and between find_related and find_related_claims. Descriptions generally differentiate them well.

Naming Consistency4/5

Tool names follow a consistent verb_noun pattern with methodist_ prefix for research workflow tools, though 'paginate' and 'methodist' (without underscore) are minor deviations.

Tool Count2/5

35 tools is too many for a single server's scope, exceeding the 25+ threshold. The server combines two major workflows (literature search and research process guidance), which would be better split.

Completeness4/5

The tool set covers a wide range of operations: multiple search modes, document submission and versioning, topic exploration, evidence checking, methodology search, and research guidance. Minor gaps include lack of document deletion or metadata update without new version.

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