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OpenArx

by ai.openarx

search_semantic

Pure semantic (vector) search — best for paraphrased queries, concept exploration, "papers arguing X" type questions. Uses dense vector similarity via Gemini or SPECTER2 embeddings. Skips BM25 fusion which can introduce term-matching noise. For exact terms use "search_keyword". For mixed queries use "search".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return
queryYesSearch query — concepts, paraphrased ideas, "papers arguing X"
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.
strategyNo'fast' (~1s) skips reranker; 'rerank' (~10s) applies cross-encoder for higher relevancefast
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. 'contentType': mix methodology/results/etc.document
vectorModelNo'gemini' for general semantic queries (default); 'specter2' for scientific paper similarity. ★ COVERAGE DIFFERS: the specter2 space does not cover the whole corpus — 77,782 chunks carry no vector in it and are therefore INVISIBLE to a specter2 search, not merely ranked lower. An empty result there means 'not indexed in this space', which is indistinguishable from 'nothing similar exists'. Use gemini when completeness matters; use specter2 to re-rank or corroborate.gemini
maxPerDocumentNoMax chunks per single key (only when diversifyBy=document)

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It clearly discloses the core behavioral trait — dense vector similarity using Gemini or SPECTER2 — and the decision to skip BM25 fusion to avoid term-matching noise. However, it does not mention material limitations like the SPECTER2 coverage gap (which is only in the parameter schema) or return/pagination behavior.

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?

Three sentences, front-loaded with purpose, followed by usage guidance and alternatives. Every sentence earns its place with no repetition of schema details.

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 rich 100%-covered schema that documents all 14 parameters, the description provides the missing context: tool selection, embedding behavior, and when to prefer siblings. No output schema exists, but the detail parameter covers output granularity, so the combined schema+description is highly complete.

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 100%, so the baseline is 3. The description adds some conceptual meaning (e.g., 'paraphrased queries' aligns with the query parameter), but most parameter semantics are already fully documented in the schema, including the important vectorModel coverage warning.

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 opens with 'Pure semantic (vector) search', a specific verb+resource that immediately states the tool's function. It further distinguishes from siblings by naming 'search_keyword' and 'search' as alternatives for exact and mixed 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?

Provides explicit guidance on when to use: 'best for paraphrased queries, concept exploration, "papers arguing X" type questions'. It also gives direct alternatives for other scenarios: 'For exact terms use "search_keyword". For mixed queries use "search".' This fully orients an agent to select the right tool.

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