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openaq-mcp-server: list parameters

openaq_list_parameters
Read-onlyIdempotent

Catalog of every measurable pollutant and its canonical unit: id, code, display name, unit, and a one-line description (pm25, pm10, o3, no2, so2, co, bc, and ~38 more). This is the unit-disambiguation reference — the same pollutant exists under several ids with different units (CO is id 4 in µg/m³, id 8 in ppm, id 102 in ppb), so use this to pick the exact parametersId for openaq_find_locations / openaq_get_readings / openaq_get_measurements and to interpret a reading's unit. A small bounded catalog fetched live from OpenAQ.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoCase-insensitive filter over the bounded parameter catalog (~44) by code, display name, and description (e.g. "pm" for particulates, "ozone", "co"). Omit to list everything.
pollutantsOnlyNoWhen true, exclude meteorological/auxiliary parameters (temperature, humidity, wind, pressure, particle-count channels) and return only air pollutants. Default false (full catalog).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
noticeNoGuidance when the query matched nothing.
parametersNoMatching parameters. Multiple rows can share a name with different ids/units — pick the id whose unit you want.
totalCountNoTotal parameters matched after filtering.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, covering safety. The description adds behavioral context: 'A small bounded catalog fetched live from OpenAQ' reveals that the tool performs a live fetch and returns a limited set, which is useful for planning. It also notes the default behavior for pollutantsOnly via the parameter, but that is schema-covered. No contradictions with annotations; the added info is valuable but not extensive.

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 three sentences with zero fluff. It leads with the tool's core function (catalog of pollutants and units), then explains purpose and usage, and ends with a note on the bounded live fetch. Every sentence earns its place, and the critical unit-disambiguation point 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?

For a simple-list tool with a rich output schema, the description is complete. It covers the tool's output content (fields), the filtering parameters, the use cases, and the bounded nature. It does not need to describe return format because the output schema defines it. There are no obvious gaps an agent would need to know to call it correctly.

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?

Schema description coverage is 100%, so the baseline is 3. The description does not add new parameter meanings beyond what the schema already provides: the query parameter's filtering behavior and the pollutantsOnly flag are both fully documented in the schema with examples and defaults. The description reuses the same information without extension, so no extra semantic value is added.

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 lists a catalog of pollutants with canonical units, specifying the exact fields (id, code, display name, unit, description). It distinguishes itself from siblings like openaq_list_countries by focusing on pollutant parameters and unit disambiguation, and it explicitly mentions the sibling tools (openaq_find_locations, openaq_get_readings, openaq_get_measurements) that it supports. Purpose is unambiguous.

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 explicit when-to-use guidance: 'use this to pick the exact parametersId for openaq_find_locations / openaq_get_readings / openaq_get_measurements and to interpret a reading's unit.' It also indicates that omitting the query lists everything, which clarifies a common usage. It does not explicitly state when not to use it or name alternatives to avoid, but the context is strong enough to route an agent correctly.

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

A4.4/5.0
Disambiguation5/5

Each tool serves a distinct purpose: catalog discovery (list_countries, list_parameters), station lookup (find_locations), current data (get_readings), historical data (get_measurements), and DataCanvas query support (dataframe_describe, dataframe_query). No two tools overlap in function.

Naming Consistency4/5

The prefix 'openaq_' is consistent, and most tools follow a verb_noun pattern (find_locations, get_measurements, list_countries). However, 'dataframe_describe' and 'dataframe_query' invert this to noun_verb, a minor deviation that could confuse agents.

Tool Count5/5

Seven tools covers the full workflow (discover, locate, read current/historical, and handle large data) without redundancy. This is a well-scoped surface for an air quality data server.

Completeness5/5

The set provides end-to-end capability: metadata discovery, location search, current conditions, historical time series, and a mechanism for large SQL queries. No critical gaps like missing update/delete operations exist because the server is read-only by design.