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Epa Emissions

by io.github.pipeworx-io

Ghg Emissions By Sector

ghg_emissions_by_sector
Read-onlyIdempotent

Total greenhouse-gas emissions by INDUSTRY SECTOR within a US state (EPA GHGRP) — answers 'which sectors emit the most in '. Sectors: Power Plants, Refineries, Chemicals, Metals, Minerals, Pulp and Paper, Petroleum and Natural Gas Systems, Waste, etc. Returns each sector's total metric tons CO2e and facility count, ranked. State-scoped (pass a state); data lags ~1.5y so the latest full year (~2023) is auto-selected unless year is given.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoReporting year (default: latest available, ~2023).
stateYesUS state — 2-letter code ("TX") or full name ("Texas"). Required (state-scoped, not national).
sectorNoOptional — filter to one sector by name (e.g. "Power Plants", "Refineries", "Chemicals").

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
stateYesState name searched
sectorsYesEmissions aggregated by industry sector
facilitiesYesComplete facility records
facility_countYesTotal number of facilities returned

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds context: data source (EPA GHGRP), data lag (~1.5y), auto-selection of latest year, return fields (metric tons CO2e, facility count). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with purpose, then details. Every sentence adds value. Could be slightly tighter, but very effective.

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?

Given the tool's simplicity (3 params, 1 required, no enums, output schema exists), the description fully covers purpose, inputs, data source, lag, return format, and ranking. No gaps.

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 3 parameters with descriptions. The description adds value by explaining state accepts 2-letter code or full name, year defaults to latest, and sector is an optional filter. Examples reinforce usage.

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 clearly states the tool provides total greenhouse-gas emissions by industry sector within a US state, answering a specific question ('which sectors emit the most in <state>'). It names sectors and distinguishes from facility-level tools (sibling exists).

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 gives clear context on when to use (state-scoped, sector-level emissions) and includes examples. It does not explicitly state when not to use or list alternatives, but the sibling list includes facility-level tools, implying boundary.

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
Disambiguation3/5

The tool set contains multiple groups with overlapping purposes (e.g., ask_pipeworx/ask_pipeworx_grounded/deep_research for data queries, multiple Polymarket tools, and memory tools). However, detailed descriptions help differentiate them, so ambiguity is moderate but not severe.

Naming Consistency2/5

Naming conventions are inconsistent: some tools follow verb_noun (ask_pipeworx, compare_entities), others use domain-prefixed noun_verb (ghg_emissions_by_sector, polymarket_arbitrage). This mix, combined with a server name that doesn't match the tool domain, makes the naming pattern unclear.

Tool Count1/5

With 35 tools, the server is overloaded, especially given its name 'Epa Emissions' which suggests a narrow focus. Only 5 tools (ghg_*, tri_*) are related to emissions; the rest are unrelated, making the count inappropriate for the server's assumed purpose.

Completeness2/5

For an EPA/emissions server, the tool set is incomplete: it lacks other emissions data (e.g., air quality, water quality, enforcement). The inclusion of many unrelated tools (e.g., Polymarket, memory) does not compensate for missing core emissions coverage.

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