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LiveDataLink

by io.github.blackboxfoundry

paper_search

Read-onlyIdempotent

Search ~250M scholarly works (papers, preprints, datasets) live via OpenAlex by keyword across title, abstract, and full text, with optional author, year, and open-access-only filters. Ranked by relevance. Returns each work's OpenAlex id, DOI, title, authors, venue, year, citation count, and a free full-text URL when open access. Use paper_fulltext_search to search inside the locally indexed open-access corpus.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoOptional exact publication year, e.g. 2023.
limitNoMaximum rows to return (default 25, max 100).
queryNoKeywords across title/abstract/fulltext, e.g. 'attention mechanism transformers', 'CRISPR off-target'.
authorNoOptional author-name fragment, e.g. 'Hinton', 'Doudna'.
open_access_onlyNoIf true, only return open-access works (default false).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds useful context beyond annotations: live OpenAlex source, corpus scale, relevance-based ranking, and the exact fields returned including conditional full-text URL.

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?

Every sentence earns its place: scope, filters, ranking, return fields, and sibling differentiation are packed into four efficient sentences. No redundancy or filler.

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 no-output-schema tool, the description compensates well by listing all return fields. Combined with full param descriptions in the schema and rich annotations, an agent has everything needed to decide, invoke, and interpret the result.

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 schema already documents all five parameters clearly. The description's mention of author, year, and open-access-only filters adds little beyond the schema, but it does reinforce query scope with title/abstract/fulltext.

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 uses a specific verb ('Search') with a clear resource and scope: ~250M scholarly works via OpenAlex across title, abstract, and full text. It distinguishes itself from paper_fulltext_search, making the tool's role unmistakable.

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?

The description states exactly when to use this tool versus the sibling: use paper_fulltext_search for the locally indexed open-access corpus, while this tool handles live global search. This provides an explicit alternative with a clear route for an agent.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources