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LiveDataLink

by io.github.blackboxfoundry

entity_resolve

Read-onlyIdempotent

Resolve a company across US government sources in one call. Searches SEC EDGAR, EPA ECHO, and the sanctions lists by name and returns the candidate match and strong identifiers (SEC CIK, ticker, EPA registry id) found in each. Use this to confirm WHO an entity is and gather its IDs before pulling detail. Matches are name-based candidates to verify, not certain identity links.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesCompany / organization name, e.g. 'Chevron Corporation', 'Acme Trucking LLC'.
stateNoOptional 2-letter US state to disambiguate location-based sources (e.g. 'TX').

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the readOnlyHint/idempotentHint annotations, it discloses that results are 'name-based candidates to verify, not certain identity links,' which is critical for interpreting output. It also clarifies that the tool aggregates multiple government sources rather than returning a verified single identity.

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 with each earning its place: the first states the action and scope, the second specifies sources and output, and the third gives the crucial interpretation caveat. The key guidance is front-loaded and there is no redundant 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 two-parameter read-only tool with no output schema, the description fully covers what the tool does, which sources it touches, what it returns, and how to interpret the results. An agent can correctly decide to call it and understand the response without needing additional context.

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 name and state. The description adds that matching is by name and that state is used for 'location-based sources,' but this is contextual rather than a significant expansion of parameter semantics.

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?

States a specific verb and resource: 'Resolve a company across US government sources in one call.' It names the exact sources searched (SEC EDGAR, EPA ECHO, sanctions lists) and the output (candidate match plus identifiers), which clearly differentiates it from generic lookup or detail tools like entity_dossier or company_info.

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?

Explicitly says to use it to 'confirm WHO an entity is and gather its IDs before pulling detail,' giving a clear intended workflow. It does not name alternative tools or state when not to use it, 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.

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