Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

supplier_esg_scorecard

Read-onlyIdempotent

One-call, source-linked ESG / supplier-risk signal read for a company. Joins three public-record governance-signal legs: EPA ECHO/FRS environmental compliance (significant-non-compliance flags, non-compliant quarters, penalties, and formal enforcement actions across the company's facilities, with correct filtering so clean 'No Violation Identified' statuses are never flagged), product recalls (CPSC consumer products + openFDA drug/device/food, keyed by the company), and federal-court litigation (CourtListener v4 dockets whose caption actually names the company). Returns a readable scorecard across Environmental / Product-safety / Litigation dimensions with a rolled-up read (LOW / MODERATE / ELEVATED ESG risk signals) and the evidence behind each. A leg that fails is noted, not fatal. This is informational public-record synthesis of ESG/supplier-risk signals, not an ESG rating, credit/background report, or investment advice. Review source terms before redistributing results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateNoOptional 2-letter state to disambiguate the EPA facility search (e.g. 'PA').
companyYesCompany / supplier name to score (e.g. 'US Steel', 'Tyson Foods').

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses meaningful behavior: a failing leg is noted rather than fatal, litigation is only counted when the docket caption names the company, clean 'No Violation Identified' EPA statuses are never flagged, and the output includes a rolled-up LOW/MODERATE/ELEVATED read plus supporting evidence. It also disclaims that this is not an ESG rating or investment advice. No contradiction with annotations exists.

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?

The description is dense but well-organized: opening purpose, three source legs, output shape, failure semantics, and disclaimers. Every sentence adds necessary context, and the most important scope information is front-loaded.

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?

With no output schema, the description correctly carries the burden of explaining return values: dimensions, risk levels, evidence, and leg-failure handling. It also covers source nuances and legal limitations. For a tool with this complexity and no structured output schema, the description is complete enough for correct invocation.

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?

The schema already documents both parameters with 100% coverage, so the baseline is 3. The description adds a little context around how the company parameter is used (keyed by the company, court caption matching) but mostly reinforces schema descriptions like state disambiguating the EPA facility search. No substantial new parameter-level detail is provided.

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 is very specific: it is a 'One-call, source-linked ESG / supplier-risk signal read' that joins three named public-record legs and returns a scorecard with Environmental / Product-safety / Litigation dimensions. It clearly identifies the resource, the action, and the output, distinguishing it from single-source tools like epa_facility_compliance or cpsc_recall_search.

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?

The description clearly frames the tool as a consolidated company-level ESG/supplier-risk read covering EPA compliance, product recalls, and federal litigation, so the intended use is evident. It does not explicitly name alternative tools or when-not-to-use conditions, but the scope is clear enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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