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by ai.openarx

find_methodology

Find methodology approaches for a specific research task. Returns structured method-level results (not raw chunks): method name, key idea, dataset used, performance metric. Filters by task domain, dataset, metric. Built on LLM-classified contentType=methodology chunks combined with benchmark results JOIN. Use this instead of search when you want HOW researchers approach a problem rather than 10 papers about it. Note: surfaces any chunk classified as methodology, including ones where the task is mentioned only as a toy example. Filter by category (e.g. cs.CV for image tasks) to narrow scope. This searches EXISTING papers for methods others have published (literature search) — it is NOT a guide for conducting your own research: for a step-by-step scientific method tailored to your own research question, start with the methodist door.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesResearch task: "relation extraction", "question answering", "image classification"
limitNoMax results to return
dateToNoFilter: published on or before (ISO date)
detailNo'standard'/'full' invoke an extra LLM extraction step to surface method_name + key_idea (~1.5s overhead). 'minimal' skips it.standard
metricNoEvaluation metric: "F1", "accuracy", "BLEU"
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.
datasetNoSpecific dataset name: "SQuAD", "ImageNet", "GLUE"
dateFromNoFilter: published on or after (ISO date)
frameworkNoML framework filter: "PyTorch", "TensorFlow"
categoriesNoFilter by arXiv categories (e.g. cs.AI, cs.LG)

TDQS

A4.9/5.0
Behavior5/5

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

No annotations provided, so description fully covers behavior: LLM-classified chunks, JOIN with benchmark results, extra LLM extraction step for detail parameter, and attribution requirements for run_id. No contradictions.

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?

All sentences are informative and necessary, no redundancy. Front-loaded with main purpose, then specifics, then caveats. Efficiently structured.

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?

For a 10-parameter tool with no output schema, the description adequately explains return structure (method name, key idea, dataset, performance metric), covers edge cases (toy examples), and mentions benchmark JOIN. Complete for its complexity.

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 100%, so baseline is 3. Description adds context by explaining the extra LLM step for detail and the run_id attribution mechanism, providing value beyond 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 it finds methodology approaches for a research task, returns structured method-level results, and distinguishes itself from sibling tools like search and methodist by specifying when to use it instead.

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 when to use (want HOW rather than papers) and when not to (not for conducting own research, use methodist instead), plus provides filtering advice and caveats about toy examples.

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.

Resources