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LiveDataLink

by io.github.blackboxfoundry

paper_details

Read-onlyIdempotent

Get full catalog metadata for a single scholarly work by OpenAlex id (e.g. 'W2741809807') or DOI (e.g. '10.1038/nature12373'). Returns title, authors, venue, year, citation count, open-access status, and a free full-text URL when available. For a bare arXiv id, use paper_get_text with paper_key 'arxiv:' to read indexed text, or paper_search by title for OpenAlex metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesOpenAlex id ('W...') or DOI ('10.x/...'). For arXiv ids, use paper_get_text or paper_search instead.

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by enumerating the returned metadata fields and noting that the full-text URL is provided only 'when available.' This goes beyond the schema, though it does not discuss not-found or error 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, zero filler. The core purpose and identifier formats are front-loaded, the return value set is summarized in one clause, and the arXiv alternative routing is placed at the end without distracting from the main use case.

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 single-parameter, read-only lookup with no output schema, the description is complete: it states exactly what fields to expect, which identifiers are valid, and what to use instead for arXiv ids. An agent has enough information to select and invoke the tool correctly without opening any additional schemas.

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

Parameters5/5

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

The schema covers this single 'id' parameter 100%, and the description adds concrete examples ('W2741809807', '10.1038/nature12373') plus a precise redirect for arXiv ids. For a one-parameter tool, this leaves no ambiguity about what values are accepted and which identifiers should not be passed.

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 a specific verb and resource: 'Get full catalog metadata for a single scholarly work,' and immediately clarifies the accepted identifier forms (OpenAlex id or DOI). It also distinguishes itself from sibling tools by explicitly pointing to paper_get_text and paper_search for arXiv ids.

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 gives an explicit when-not-to-use condition: 'For a bare arXiv id, use paper_get_text with paper_key arxiv:<id> to read indexed text, or paper_search by title for OpenAlex metadata.' This directly routes an agent away from this tool to the correct alternatives, which is exactly what usage guidance should do.

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