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OpenArx

by ai.openarx

search_keyword

Pure keyword (BM25) search — fastest option, optimal for exact-term lookups: paper titles, author names, method names (e.g. "LoRA", "RLHF"), arXiv IDs. Does NOT use semantic vectors. Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return
queryYesSearch query — exact terms work best (method names, IDs, titles). NOTE: BM25 ranks by chunk-level term frequency; for canonical paper lookup by exact name (e.g. "LoRA" → original LoRA paper), prefer find_by_id by arxivId or title-search. This tool may surface papers that mention the term frequently but are not the canonical source.
dateToNoFilter: published on or before (ISO date)
detailNo'minimal' = id+title+snippet+score. 'standard' = adds metadata + chunkContext. 'full' = adds entities/selfContained/scores/licenses mapstandard
facetsNoIf true, return facets block: count breakdown by contentType + top entities mentioned
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.
dateFromNoFilter: published on or after (ISO date)
entitiesNoSoft filter by entity (method names like "BERT", datasets like "SQuAD", metrics like "BLEU"), case-insensitive. Matching chunks rank first; chunks with no entities recorded (legacy gap) fall to the bottom rather than being dropped; chunks with non-matching entities are excluded.
categoriesNoFilter by arXiv categories (e.g. cs.AI, cs.LG)
contentTypeNoFilter chunks by type. Use [methodology] for HOW researchers approach a problem; [results] for OUTCOMES; [survey, background] for context
diversifyByNo'document' (default): max N chunks per paper. 'keyConcept': diversify by main idea (good for landscape view). 'contentType': mix methodology/results/etc.document
maxPerDocumentNoMax chunks per single key (only when diversifyBy=document)

TDQS

A4.7/5.0
Behavior4/5

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

No annotations exist, so description fully carries the burden. It clearly explains the tool uses BM25 algorithm, does not use semantic vectors, and describes ranking behavior (chunk-level term frequency). It also notes the limitation of surfacing frequent-term papers over canonical sources. While lacking details on authentication or rate limits, it provides sufficient behavioral context for an agent.

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 a single, well-structured paragraph that front-loads the core purpose. Every sentence adds value (e.g., 'Does NOT use semantic vectors', explicit alternatives). No redundancy or filler. Length is appropriate for the complexity.

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 12 parameters (mostly optional) and no output schema, the description thoroughly explains each parameter's behavior, the underlying algorithm, and relationship to sibling tools. It covers when to use, how to use, and potential pitfalls (e.g., BM25 not guaranteeing canonical results). The description feels complete for an AI agent to make an informed decision.

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 3 applies. However, the description adds significant context beyond the schema: for 'query' it explains BM25 ranking and alternative tools; for 'detail' it enumerates content levels; for 'run_id' it clarifies attribution; for 'entities' it describes soft filtering and legacy handling. This extra value justifies a 4.

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?

Description starts with 'Pure keyword (BM25) search — fastest option, optimal for exact-term lookups' and lists specific use cases (paper titles, author names, method names, arXiv IDs). It distinguishes itself from siblings by explicitly stating it does not use semantic vectors and directing users to alternatives for conceptual queries.

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?

Explicit guidance: 'Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".' Additionally, the query parameter description warns about BM25 behavior and when to prefer find_by_id for canonical papers.

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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