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AlpineDataWorks Intelligence Server

Superfund Contamination Load

adw.adw_419
Read-only

Returns a 0-100 Superfund contamination-load score (EPA SEMS status-change velocity — NPL proposals, deletions, withdrawals, NFRAP determinations — z-scored vs a trailing 100-day baseline) with recent_100d_status_changes, z_score, active_final_npl_site_count, and top_epa_region_recent. Call when the user asks about Superfund/NPL activity, contaminated sites, or EPA cleanup pressure, or when timing environmental due diligence, pollution-liability underwriting, or remediation bids. Updates: daily.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoOptional: return a daily HISTORY series of the last N days (up to 5 years of real archived data) instead of the current snapshot. History requires Gold tier; without it, the current snapshot is returned.

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true, but the description adds substantial behavioral context beyond that: the scoring formula, the returned fields, the daily update cadence, and the Gold-tier requirement for history. This gives the agent a complete picture of the tool's behavior and constraints.

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 a single information-dense sentence, front-loaded with the core purpose, followed by explicit use cases and a refresh note. No filler or redundant repetition of the schema—every clause adds value.

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 compensates by listing all return fields (recent_100d_status_changes, z_score, active_final_npl_site_count, top_epa_region_recent) and the score's meaning. It also explains the parameter's behavior and update frequency, making the tool fully self-contained for an agent to invoke 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 coverage is 100% for the single optional `days` parameter, which is already described in the schema. The description adds extra context by noting that history requires Gold tier and that the current snapshot is returned without it, which is not in the schema, so it earns above the baseline of 3.

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 ('Returns') and a clear resource ('0-100 Superfund contamination-load score'), and explains the scoring methodology (EPA SEMS status-change velocity z-scored vs a trailing 100-day baseline). It also lists the output fields, making its purpose unambiguous and clearly distinct from any sibling tool.

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 explicitly states when to call the tool: when users ask about Superfund/NPL activity, contaminated sites, or EPA cleanup pressure, or when timing environmental due diligence, pollution-liability underwriting, or remediation bids. However, it doesn't mention when not to use it or name alternatives, so it falls just short of a 5.

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

B3.3/5.0
Disambiguation1/5

With 318 tools named adw.adw_###, agents cannot tell them apart without reading full descriptions. Multiple tools cover the same domain (e.g., at least three USD strength scores: adw_055, adw_250, adw_580; four supply-chain stress scores: adw_009, adw_019, adw_020, adw_547), making misselection highly likely.

Naming Consistency3/5

The vast majority follow a consistent numeric ID pattern (adw.adw_###), but a small set breaks this with descriptive snake_case names (adw.catalog, adw.sample, adw.county_cancer, etc.). The numeric IDs are predictable but convey no semantic meaning, mixing with the few named tools and creating moderate inconsistency.

Tool Count1/5

318 tools is far beyond any reasonable scope for an intelligence server; even the largest sophisticated APIs rarely exceed 50. This extreme count suggests poor curation and will overwhelm agents with choice, making efficient tool selection impractical.

Completeness3/5

The server covers an extremely broad range of domains (crypto, macro, supply chain, healthcare, climate, county demographics), and includes discovery tools like adw.catalog and adw.sample. However, the surface is redundant and not systematically complete—many overlapping indices exist while other potentially valuable operations (e.g., raw data export, historical trend queries) are missing, leaving moderate gaps.

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