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Epa Emissions

by io.github.pipeworx-io

Ghg Facility Emissions

ghg_facility_emissions
Read-onlyIdempotent

The biggest greenhouse-gas-emitting facilities in a US state (EPA GHGRP), ranked by total CO2-equivalent emissions. Returns each facility's name, location, industry sector(s), and total metric tons CO2e. State-scoped (pass a state). Data lags ~1.5 years — latest full year is auto-selected (currently 2023) unless a year is given.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoReporting year (default: latest available, ~2023).
limitNoMax facilities to return, ranked by emissions (default 20, max 200).
stateYesUS state — 2-letter code ("TX", "CA") or full name ("Texas"). Required (this is state-scoped, not national).
facility_nameNoOptional — filter to facilities whose name contains this text.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of facilities returned
stateYesState name searched
facilitiesYesArray of facility records with emissions data

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint. The description adds valuable context: data lags ~1.5 years, returns facility details, and is state-scoped. 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?

Two sentences, well-structured, front-loaded with purpose. Every sentence adds value without redundancy.

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 presence of an output schema and annotations, the description is complete: explains data source (EPA GHGRP), ranking, return fields, state scope, data lag, and optional parameters. 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 coverage is 100% with each parameter described. The description reinforces meaning by stating 'biggest', 'ranked by total CO2-equivalent emissions', and provides example values. It adds context beyond the schema.

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 it returns the biggest GHG-emitting facilities in a US state, ranked by emissions, including facility details. This distinguishes it from siblings like ghg_emissions_by_sector, which likely aggregates by sector.

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 specifies that it is state-scoped and requires a state parameter, and mentions data lag and auto-selected year. It implicitly guides when to use this tool (facility-level data) but does not explicitly contrast with siblings like ghg_emissions_by_sector.

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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