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openaq-mcp-server wraps the OpenAQ v3 API to expose measured air quality — physical-sensor observations from government reference monitors and research-grade sensors worldwide. It is the ground-truth counterpart to a modeled air-quality grid: where a model gives a concentration anywhere, OpenAQ gives an actual reading from a physical monitor — sparser, unevenly distributed, but real.

Coverage is uneven and honest. An empty result means there is no monitoring there, not that the air is clean — every discovery tool says so, and points to a modeled fallback (open-meteo-mcp-server's air-quality tool) for anywhere-coverage.

Tools

Five domain tools cover the workflow — discover stations, read current values, pull history, and resolve the two catalogs (pollutant units, country coverage) — plus two DataCanvas tools for SQL over historical series too large to inline. The data model is location → sensor → parameter; the server hides the sensor layer so you think in stations and parameters, never sensor ids.

openaq_find_locations

Find air-quality monitoring stations (measured by physical sensors, not modeled) and the parameters each one reports.

• Three search scopes — coordinates + radius (near-me), bbox (area sweep), or iso country code; at least one is required
• radius is in metres, 1–25000 (the API hard-caps at 25000); larger areas need bbox, which returns no distance
• parametersId narrows to stations that measure a given parameter (each returned station still lists all its sensors)
• Returns each station's id, name, coordinates, distance (when searching by coordinates), country, provider, isMonitor/isMobile, the parameters its sensors measure with units, and the datetimeFirst/datetimeLast data span
• Empty result means no coverage, not clean air — widen the radius, check openaq_list_countries, or fall back to the modeled open-meteo air-quality tool

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openaq_get_readings

Latest value per sensor at a station — the current-conditions tool.

• Pass a locationId from openaq_find_locations, or coordinates + parametersId to auto-resolve the nearest station (within 25km) that measures that parameter
• The raw OpenAQ latest feed is keyed only by sensor id; this tool joins it against the station's sensor → parameter → unit map, so every value carries its pollutant and unit
• With locationId, parametersId optionally filters the returned values to one parameter; omit it for all sensors
• Each value carries its UTC and local timestamp plus the station's datetimeLast — recency varies by station, so "latest" may be minutes or hours old

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openaq_get_measurements

Historical measurement series for one pollutant at one station over a date range — for trend analysis and "was last week worse than the monthly average?".

• Pass a locationId and a parametersId; the tool resolves the station's sensor for that parameter internally (v3 series are sensor-scoped, but you think in stations)
• aggregation: raw (every reported value), hourly, or daily — hourly/daily add a per-bucket statistical summary (min, median, max, mean, sd)
• datetimeFrom/datetimeTo accept a date (YYYY-MM-DD) or full UTC timestamp (YYYY-MM-DDTHH:MM:SSZ); omit for the most recent values
• Values carry their unit; the server never converts between µg/m³, ppm, and ppb (the conversion is gas- and temperature-dependent)
• Large ranges spill to a DataCanvas — see below

DataCanvas spill workflow

A multi-month raw series can be thousands of rows — too large to inline without blowing context, and a fixed slice would blind the agent to the rest. When a series exceeds the inline preview (100 rows), openaq_get_measurements stages the full set on a DuckDB-backed DataCanvas and returns:

• a preview (series, capped at 100 rows) plus rowCount and the totalCount enrichment,
• truncated: true, canvasId, and a tableName of the form measurements_<sensorId>.

You then query the full set with the two consumer tools:

Pass a prior canvas_id back into openaq_get_measurements to stage a second station's series on the same canvas (as measurements_<otherSensorId>), then JOIN/UNION the two in one query to compare stations.

Requires CANVAS_PROVIDER_TYPE=duckdb. Without it, openaq_get_measurements still returns the truncated preview plus a notice (it does not fail), and the two dataframe tools return a canvas_unavailable error directing you to enable DuckDB.

openaq_dataframe_query is read-only by design — a four-layer SQL gate rejects writes, DDL, and file/network table functions; only a single SELECT runs.

Resources and prompts

All resource data is also reachable via tools — both resources mirror tool output, so tool-only MCP clients lose nothing. There are no prompts: this is a data-lookup domain with no recurring analysis template that earns one (a WHO-guideline health snapshot is a cross-server workflow, not localized here).

Features

Built on @cyanheads/mcp-ts-core:

• Declarative tool and resource definitions — single file per primitive, framework handles registration and validation
• Unified error handling — handlers throw, framework catches, classifies, and formats
• Typed error contracts per tool — each network tool declares reason/code/when/recovery, so failures carry a concrete next move
• Pluggable auth (none, jwt, oauth) and structured, request-scoped logging with optional OpenTelemetry tracing
• STDIO and Streamable HTTP transports from one codebase

OpenAQ-specific:

• Single typed client over the OpenAQ v3 REST API with X-API-Key auth, retry with rate-limit-calibrated backoff, and OpenAQ-specific error classification (clean-JSON 404 → NotFound; the Python-repr 422 body → ValidationError; the plain-text 500 on bad coordinates → transient ServiceUnavailable)
• Hides the v3 location → sensor → measurement hierarchy — openaq_get_measurements resolves a station + parameter to the underlying sensor; openaq_get_readings joins the latest feed against the sensor map so every value is labeled
• DataCanvas spillover for large measurement series, queryable with read-only DuckDB SQL
• Coordinates and radius are bounded in Zod at the edge — OpenAQ returns an opaque plain-text 500 for out-of-range input, so the server rejects it cleanly before the call

Agent-friendly output:

• Measured-vs-modeled framing in every discovery tool — an empty result is stated as no coverage, not clean air, with a pointer to the modeled fallback, so an agent never misreads sparse data as a clean reading
• Units travel with every value, never converted — the same pollutant has multiple parameter ids for different units (co is id 4 µg/m³, id 8 ppm, id 102 ppb), so parametersId is the precise selector and openaq_list_parameters maps pollutant + unit → id
• Chainable ids and staleness signals — location id → readings/measurements, sensor id → history; datetimeLast and per-value timestamps expose how fresh "latest" actually is
• Capped lists disclose truncation (totalCount, truncated) via framework enrichment, reaching both the structured and text output surfaces

Getting started

Public Hosted Instance

A public instance is available at https://openaq.caseyjhand.com/mcp — no installation required. Point any MCP client at it via Streamable HTTP, with this client config:

{
  "mcpServers": {
    "openaq-mcp-server": {
      "type": "streamable-http",
      "url": "https://openaq.caseyjhand.com/mcp"
    }
  }
}

Self-hosted

An OpenAQ v3 API key is required — sent as the X-API-Key header on every request. Get a free key from your OpenAQ Explorer account.

Add the following to your MCP client configuration file.

{
  "mcpServers": {
    "openaq-mcp-server": {
      "type": "stdio",
      "command": "bunx",
      "args": ["@cyanheads/openaq-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "OPENAQ_API_KEY": "your-api-key"
      }
    }
  }
}

Or with npx (no Bun required):

{
  "mcpServers": {
    "openaq-mcp-server": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@cyanheads/openaq-mcp-server@latest"],
      "env": {
        "MCP_TRANSPORT_TYPE": "stdio",
        "OPENAQ_API_KEY": "your-api-key"
      }
    }
  }
}

Or with Docker:

{
  "mcpServers": {
    "openaq-mcp-server": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "MCP_TRANSPORT_TYPE=stdio",
        "-e", "OPENAQ_API_KEY=your-api-key",
        "ghcr.io/cyanheads/openaq-mcp-server:latest"
      ]
    }
  }
}

To enable DataCanvas SQL over large measurement series, add "CANVAS_PROVIDER_TYPE": "duckdb" to env.

For Streamable HTTP, set the transport and start the server:

MCP_TRANSPORT_TYPE=http MCP_HTTP_PORT=3010 OPENAQ_API_KEY=your-api-key bun run start:http
# Server listens at http://localhost:3010/mcp

Prerequisites

• Bun v1.3.0 or higher (Node.js v24+ also works at runtime).
• A free OpenAQ v3 API key — sign up at explore.openaq.org.

Installation

1. Clone the repository:

git clone https://github.com/cyanheads/openaq-mcp-server.git

2. Navigate into the directory:

cd openaq-mcp-server

3. Install dependencies:

bun install

4. Configure environment:

cp .env.example .env
# edit .env and set OPENAQ_API_KEY

Configuration

All configuration is validated at startup via Zod schemas. Key environment variables:

See .env.example for the full list of optional overrides.

Running the server

Local development

• Build and run:

  bun run rebuild
  bun run start:http   # or start:stdio
  

• Run checks and tests:

  bun run devcheck   # Lint, format, typecheck, security, changelog sync
  bun run test       # Vitest test suite
  bun run lint:mcp   # Validate MCP definitions against spec
  

Docker

docker build -t openaq-mcp-server .
docker run --rm -e OPENAQ_API_KEY=your-api-key -p 3010:3010 openaq-mcp-server

The image defaults to HTTP transport, stateless session mode, and logs to /var/log/openaq-mcp-server. The @duckdb/node-api runtime dependency ships in the image, so DataCanvas works once CANVAS_PROVIDER_TYPE=duckdb is set. OpenTelemetry peer dependencies are installed by default — build with --build-arg OTEL_ENABLED=false to omit them.

Project structure

Development guide

See CLAUDE.md / AGENTS.md for development guidelines and architectural rules. The short version:

• Handlers throw, framework catches — no try/catch in tool logic
• Use ctx.log for request-scoped logging, ctx.state for tenant-scoped storage
• Register new tools and resources in the createApp() arrays
• Wrap the OpenAQ API: validate raw → normalize to the domain type → return the output schema; surface units verbatim and never fabricate missing fields

Data & licensing

Air quality data served by this MCP server is sourced from the OpenAQ platform. Attribution to OpenAQ as the data source is required when using this server's output (OpenAQ Terms of Use).

OpenAQ aggregates measurements from hundreds of government agencies, research institutions, and other monitoring networks worldwide. Each of those upstream providers may publish its own attribution or licensing terms. The provider field returned by openaq_find_locations, openaq_get_readings, and the openaq://location/{locationId} resource identifies the originating network for each station. Downstream users are responsible for reviewing and complying with the terms of any provider whose data they use.

Contributing

Issues and pull requests are welcome. Run checks and tests before submitting:

bun run devcheck
bun run test

License

Apache-2.0 — see LICENSE for details.