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LiveDataLink

by io.github.blackboxfoundry

paper_fulltext_search

Read-onlyIdempotent

Search INSIDE the indexed open-access corpus (arXiv + PubMed Central OA full text) for a phrase or keywords and get back the matching passages, each with the paper title, authors, and a snippet around the match. This is the headline feature: agents can find where a finding or method is discussed across open-access papers. Optionally restrict to one paper by paper_key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum passages to return (default 10, max 50).
queryYesPhrase or keywords to find inside the papers, e.g. 'scaled dot-product attention', 'gradient checkpointing'.
paper_keyNoOptional: restrict the search to a single indexed paper by its corpus key, e.g. 'arxiv:2310.12345'.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavior beyond annotations: it specifies the indexed corpus scope and the return format (matching passages with title, authors, snippet). It does not mention rate limits or pagination behavior, but those are minor given the safety profile is covered.

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?

Two sentences with no wasted words. The core action and output are front-loaded in the first sentence, and the second sentence adds use-case prominence without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the schema fully documents the three parameters and annotations cover the safety profile, the description is nearly complete. It explains what the tool returns, which is important because no output schema exists. Minor gaps like empty-result behavior or query syntax details are not fatal, but they prevent a perfect score.

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's mention of opting to restrict by paper_key mirrors the schema and adds little new meaning. It provides context for the query parameter (searching inside papers) but does not materially extend the schema's parameter documentation.

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 states a specific verb ('Search'), a precise resource ('the indexed open-access corpus (arXiv + PubMed Central OA full text)'), and the output ('matching passages, each with the paper title, authors, and a snippet'). This clearly distinguishes it from sibling paper_search or paper_details by focusing on full-text passage retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides clear context for when to use the tool: 'agents can find where a finding or method is discussed across open-access papers.' It also notes the optional paper_key restriction. However, it does not explicitly name alternatives or state when not to use it (e.g., metadata-only searches), so it stops short of full exclusion guidance.

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