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OpenArx

by ai.openarx

find_code

Find papers with associated code repositories, datasets, or benchmark results. Filter by ML task, dataset, framework, or GitHub stars. Returns top items per paper (not full lists by default — use detail=full for everything).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoTask name to match against benchmark_results (e.g. "question answering")
limitNo
queryNoOptional semantic query — papers about this topic with code
dateToNoFilter: published on or before (ISO date)
detailNo'minimal' = counts + first item each. 'standard' = top-3 per type. 'full' = all arrays.standard
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 (e.g. "SQuAD", "ImageNet") — matches dataset_links.name + benchmark_results.dataset
dateFromNoFilter: published on or after (ISO date)
minStarsNoMinimum GitHub stars on at least one code_link
frameworkNo"PyTorch" / "TensorFlow" / "JAX" / etc. — matches code_links.language
categoriesNoFilter by arXiv categories (e.g. cs.AI, cs.LG)

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that by default only top items per paper are returned, and detail=full is needed for complete lists. The run_id parameter description adds context about attribution. However, it does not mention authentication, rate limits, or that it is a read-only operation.

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

Conciseness5/5

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

The description is two sentences: first covers purpose and filters, second clarifies default output behavior. It is front-loaded with key information and contains no unnecessary words.

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

Completeness4/5

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

Given 11 parameters and no output schema, the description explains the default output (top items per paper) and the detail parameter, which is sufficient. It could elaborate on the structure of return values, but the behavior is clear for a search tool.

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

Parameters4/5

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

Schema coverage is high (91%), so baseline is 3. The description adds value by explaining the detail parameter's effect ('top items per paper') and providing a detailed, context-rich explanation for run_id beyond the schema description. Other parameters are already well-described in the schema.

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 the tool finds papers with associated code, datasets, or benchmark results, and lists specific filters (ML task, dataset, framework, stars). It distinguishes itself from siblings like find_benchmark_results by focusing on code/dataset links.

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

Usage Guidelines3/5

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

The description implies when to use (when needing papers with code), but does not explicitly mention alternatives or when not to use it. No comparative guidance against sibling tools like search or find_benchmark_results is 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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