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LiveDataLink

by io.github.blackboxfoundry

epa_facility_search

Read-onlyIdempotent

Search EPA-regulated facilities by state, city, zip, and/or facility name. Returns each facility's Registry ID (needed for the other EPA tools), address, and a snapshot of its compliance status across Clean Air Act, Clean Water Act, RCRA (waste), and Safe Drinking Water programs. Provide at least one filter; broad queries (e.g. state only for a large state) may be rejected as too broad, so add a city, zip, or name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
zipNo5-digit ZIP code (e.g. '20010').
cityNoCity name (e.g. 'Washington').
nameNoFacility name or fragment (e.g. 'Pepco', 'refinery').
limitNoMaximum facilities to return (default 25, max 100).
stateNoTwo-letter state or territory code (e.g. 'DC', 'TX', 'CA').
active_onlyNoIf true, only return facilities flagged with active enforcement/compliance activity.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds beyond-annotation context by warning that broad queries may be rejected and by summarizing the returned compliance snapshot across four regulatory programs. No contradiction.

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 dense sentences: front-loaded purpose, then output value, then the key caveat. No filler, no repetition of schema descriptions, and each sentence earns its place.

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?

Even with no output schema, it names the key return fields (Registry ID, address, compliance status across named programs) and the practical filter constraint. This is complete enough for an agent to invoke it correctly.

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

Parameters4/5

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

Schema covers all 6 parameters at 100%, so the baseline is 3. The description adds meaningful parameter-relationship guidance: at least one filter is required, and state-only queries may be too broad—value beyond what the schema states.

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 ('Search') and resource ('EPA-regulated facilities') with the allowed filter dimensions. It also distinguishes itself from EPA siblings by noting the returned Registry ID is 'needed for the other EPA tools,' positioning it as the discovery entry point.

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?

Gives clear operational guidance: provide at least one filter, and broad queries may be rejected, with a concrete remedy to add city, zip, or name. It does not explicitly name sibling alternatives or say when NOT to use this tool, so it misses the top score.

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