How to Build a WebMCP Tool for Agents to Monitor Air Quality Indices in Delhi NCR

Air quality monitoring is a strong use case for agentic web tools: the data changes frequently, users need location-specific answers, and an agent can turn raw readings into alerts, summaries, and decisions. If you are exploring how to build a WebMCP tool for agents to monitor air quality indices in Delhi NCR, the key is to expose a narrow, reliable interface rather than giving an AI agent unrestricted access to websites or databases.

A production-ready tool should resolve Delhi NCR locations, retrieve current and historical measurements, normalize AQI values, identify the source and timestamp, and return machine-readable safety context. It should also handle missing stations, conflicting readings, API limits, and the difference between India’s National Air Quality Index (NAQI) and other AQI standards.

What WebMCP Means for an Air-Quality Agent

WebMCP can be understood as a tool interface that allows an AI agent to call structured web capabilities. Instead of asking an agent to scrape an air-quality page, you expose operations such as:

get_current_aqi(location, pollutant, radius_km)
get_aqi_forecast(location, date_range)
compare_locations(locations)
create_aqi_alert(location, threshold, duration)
explain_aqi(value, standard, sensitive_group)

The agent decides when to call a tool, supplies validated arguments, and receives structured results. Your server remains responsible for data retrieval, authentication, source selection, normalization, caching, and policy controls.

For Delhi NCR, this separation matters because air-quality data can differ between monitoring stations in Delhi, Noida, Ghaziabad, Gurugram, and Faridabad. A tool should never imply that one station represents the entire region without stating the station, distance, timestamp, and measurement method.

Define the Tool’s Scope Before Writing Code

Start with a narrow product requirement. A useful initial scope is:

> Given a Delhi NCR locality or coordinates, return the latest available AQI, dominant pollutant, pollutant concentrations, station metadata, data age, health category, and recommended precautions.

Avoid combining every possible feature in the first version. Forecasting, long-term trend analysis, commuter routing, and automated notifications can be added after the current-AQI workflow is reliable.

Define these decisions explicitly:

Geography: Delhi’s districts plus NCR areas in Haryana, Uttar Pradesh, and Rajasthan.
Location resolution: accept a locality, PIN code, city, or latitude/longitude.
Measurement scope: station-level readings, nearest-station readings, or an area aggregate.
AQI standard: India’s NAQI by default; clearly label any alternative standard.
Freshness: for example, mark data older than 60 or 120 minutes as stale.
Output language: English initially, with Hindi support as a useful extension.
Safety boundary: provide general public-health information, not diagnosis or individualized medical advice.

Choose Authoritative and Redundant Data Sources

A robust Delhi NCR tool should use an official or well-documented primary source whenever possible. In India, the Central Pollution Control Board (CPCB) and its National Air Quality Index ecosystem are natural sources to evaluate. Depending on access and licensing, you may also assess state pollution-control boards, municipal data portals, or reputable weather and environmental-data providers.

Before integrating a source, verify:

API availability and authentication requirements
permitted commercial and AI-agent use
update frequency and historical retention
station identifiers and geographic coordinates
pollutant units, averaging periods, and missing-value conventions
rate limits, uptime, and attribution requirements

Do not silently merge incompatible feeds. PM2.5 may be reported as a concentration over a specific averaging interval, while AQI is calculated through pollutant-specific breakpoints. Store the original value, unit, averaging period, source, and timestamp before transforming it.

A practical fallback strategy is:

1. Query the preferred official source.
2. If it fails, use a pre-approved secondary source.
3. Return the source and fallback status to the agent.
4. If no reliable reading is available, return an explicit unavailable state.

The tool should never invent a current AQI from stale or incomplete data.

Design a Machine-Readable WebMCP Contract

The interface should be predictable for both agents and developers. A JSON Schema-style contract could look like this:

{
  "name": "get_current_aqi",
  "description": "Return the latest verified air-quality reading for a Delhi NCR location.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "location": { "type": "string", "minLength": 2 },
      "latitude": { "type": "number", "minimum": 28.0, "maximum": 29.5 },
      "longitude": { "type": "number", "minimum": 76.5, "maximum": 78.0 },
      "radius_km": { "type": "number", "minimum": 1, "maximum": 50 },
      "include_pollutants": { "type": "boolean" }
    },
    "required": ["location"],
    "additionalProperties": false
  }
}

The geographic bounds above are validation aids, not a substitute for proper location resolution. NCR boundaries are administrative and operationally complex, so a geocoder should return a canonical place, state, coordinates, and confidence score.

A successful response should include both data and provenance:

{
  "location": {
    "requested": "Connaught Place",
    "resolved": "New Delhi",
    "latitude": 28.6315,
    "longitude": 77.2167
  },
  "aqi": {
    "value": 184,
    "category": "Poor",
    "standard": "India NAQI",
    "dominant_pollutant": "PM2.5"
  },
  "station": {
    "name": "Example Monitoring Station",
    "distance_km": 2.4
  },
  "observations": [
    { "pollutant": "PM2.5", "value": 112, "unit": "µg/m³" }
  ],
  "observed_at": "2026-09-03T08:30:00+05:30",
  "retrieved_at": "2026-09-03T08:36:12+05:30",
  "freshness": "fresh",
  "source": { "name": "CPCB", "url": "https://cpcb.nic.in/" }
}

Use ISO 8601 timestamps with the India time-zone offset. Agents need to distinguish when the station observed the reading from when your server retrieved it.

Build the Data Pipeline

A clean architecture separates the agent-facing tool from provider-specific integrations:

1. Tool gateway: authenticates requests, validates input, and applies quotas.
2. Location resolver: converts a locality or coordinates into a canonical search area.
3. Provider adapters: retrieve data from CPCB or approved providers.
4. Normalizer: standardizes pollutant names, units, timestamps, and status codes.
5. AQI interpreter: applies the selected Indian AQI rules and categories.
6. Cache: reduces repeated calls while preserving freshness metadata.
7. Response formatter: produces a stable schema designed for agent consumption.
8. Observability layer: records latency, failures, provider status, and tool calls.

Keep provider adapters independent. A change in one upstream API should not force you to rewrite the WebMCP contract. Store raw responses for debugging only when permitted by the provider’s terms and your retention policy.

Normalize Pollutant Data Carefully

Common pollutants include PM2.5, PM10, nitrogen dioxide, sulphur dioxide, carbon monoxide, ozone, ammonia, and lead. Normalization should address:

inconsistent spelling, such as PM_2_5, PM25, and pm2.5
units such as µg/m³ and mg/m³
null, negative, or impossible values
averaging periods
station downtime and calibration flags
duplicate observations

Do not calculate a health category from a concentration unless you have the correct pollutant-specific breakpoint table and averaging period. If the provider supplies an AQI, preserve it as a source value and identify whether your system recomputed or merely reported it.

Implement the Tool Server

The exact WebMCP runtime may vary by platform, but the backend pattern is familiar. The following TypeScript-style pseudocode illustrates the core flow:

type AqiRequest = {
  location: string;
  latitude?: number;
  longitude?: number;
  radius_km?: number;
  include_pollutants?: boolean;
};

async function getCurrentAqi(input: AqiRequest) {
  validateRequest(input);

  const place = await resolveDelhiNcrLocation(input);
  if (!place || place.confidence < 0.75) {
    return {
      status: "needs_clarification",
      message: "Please provide a more specific Delhi NCR locality or coordinates."
    };
  }

  const cacheKey = makeCacheKey(place, input.radius_km ?? 10);
  const cached = await cache.get(cacheKey);
  if (cached && !isExpired(cached, 15 * 60)) return cached;

  const raw = await providerRouter.fetchNearest({
    lat: place.latitude,
    lon: place.longitude,
    radiusKm: input.radius_km ?? 10
  });

  const normalized = normalizeObservation(raw);
  const result = buildAgentResponse(normalized, place);
  await cache.set(cacheKey, result, 15 * 60);
  return result;
}

In production, add request IDs, structured logs, circuit breakers, timeout limits, retry policies with jitter, and provider-specific error handling. A retry must not turn a temporary outage into a long agent response or a burst of upstream traffic.

Make Location Resolution NCR-Aware

“Delhi NCR” is not one monitoring point. Location resolution should support:

Delhi neighbourhoods such as Rohini, Dwarka, Lajpat Nagar, and Vasant Kunj
Noida and Greater Noida
Ghaziabad, including Indirapuram and Vasundhara
Gurugram and Manesar
Faridabad
nearby NCR districts where your data coverage is explicit

Return multiple candidate locations when a name is ambiguous. For example, a request for “Sector 18” should not automatically resolve to Noida if the user may mean Gurugram or another city.

For coordinates, select the nearest valid station but also return distance and station name. If the nearest station is offline, consider the next station only if it falls within a documented radius. Otherwise return “no reliable nearby station” instead of presenting a distant reading as local truth.

Add Agent-Friendly Health and Alert Logic

Agents need context, but context must be precise. A useful response can include:

AQI value and India NAQI category
dominant pollutant
timestamp and freshness label
station distance and data source
plain-language precautions
uncertainty or coverage warnings

For alerts, avoid embedding notification delivery inside the read operation. Use a separate tool such as create_aqi_alert with explicit parameters:

{
  "location": "Noida Sector 62",
  "threshold": 200,
  "condition": "above",
  "consecutive_readings": 2,
  "channels": ["webhook"],
  "expires_at": "2026-10-01T00:00:00+05:30"
}

Require confirmation before creating alerts, sending messages, or triggering external actions. Include deduplication keys so an agent cannot create repeated alerts after a conversational retry.

Health guidance should remain general: reduce prolonged outdoor exertion during poor air quality, consider official advisories, and advise people with respiratory or cardiovascular conditions to follow their clinician’s guidance. Do not claim that an AQI reading diagnoses exposure or guarantees safety indoors.

Security, Privacy, and Reliability Controls

An agent tool is an API and should be treated like one. Recommended controls include:

API keys or OAuth for authenticated clients
per-user and per-agent rate limits
schema validation and maximum input lengths
SSRF protection if providers are configurable
allowlisted upstream domains
secret storage outside source code
audit logs for alert creation and external actions
redaction of personal location history
CORS and origin controls for browser-based clients
timeouts and circuit breakers for every provider call

Location data may reveal home, workplace, or travel patterns. Store only what is needed, define retention periods, and disclose whether requests are logged. For Indian deployments, review the Digital Personal Data Protection Act, 2023 and applicable contractual, security, and consent obligations with qualified counsel.

Test the WebMCP Tool with Realistic Scenarios

Unit tests should cover parsing and AQI interpretation, but agent tools also need contract and failure testing. Build a test matrix for:

exact locality names and spelling variations
ambiguous locations across NCR
coordinates outside your supported boundary
no nearby active station
stale readings
missing pollutants
provider timeout and malformed JSON
conflicting station values
duplicate alert requests
Hindi or mixed-language inputs
prompt injection text inside upstream content

Test the agent experience as well as the server. Ask whether the agent can answer “What is the AQI near my office in Gurugram?” without guessing the office location. The correct result may be a clarification request, not a fabricated reading.

Monitor operational metrics such as p50 and p95 latency, provider error rate, stale-response rate, cache hit ratio, location-resolution confidence, and percentage of responses with complete provenance. Set an alert when a provider’s feed becomes unusually stale.

Improve SEO and Discoverability for the Tool

If you are publishing the tool or an accompanying developer page, document it with:

a clear tool name and one-sentence purpose
supported Delhi NCR locations
input and output schemas
examples for current AQI, comparison, and alerts
source attribution and freshness rules
error codes and clarification behavior
rate limits and authentication instructions
a changelog for schema or provider changes

Use the target phrase naturally in the introduction, one section heading, metadata, and at least one implementation example. Avoid repeating it unnaturally. Search users are likely to want both a conceptual explanation and a working integration, so include copy-pasteable schemas, API examples, and deployment notes.

Deployment Checklist

Before making the tool available to agents, confirm:

[ ] The WebMCP manifest or tool registration is valid.
[ ] Inputs reject unsupported or ambiguous locations safely.
[ ] AQI standard and pollutant units are always labeled.
[ ] Observation and retrieval timestamps are returned.
[ ] Source attribution is included.
[ ] Stale and unavailable states are explicit.
[ ] Provider terms permit your use case.
[ ] Secrets, logs, and personal data are protected.
[ ] Alerts require confirmation and are idempotent.
[ ] Responses are tested against prompt injection and malformed upstream data.
[ ] Monitoring covers latency, freshness, errors, and coverage.

Start with read-only current-AQI queries, validate the data pipeline with Delhi NCR users, and add forecasts or alerts only after you can explain every number your agent returns.

FAQ: WebMCP Air Quality Tools for Delhi NCR

Which AQI standard should the tool use in India?

Use India’s National Air Quality Index as the default for Indian users and label it clearly. If you expose US AQI or another standard, make it an explicit option rather than silently converting values.

Can I scrape a public air-quality website?

Scraping may violate terms, break frequently, and produce incomplete provenance. Prefer an official or licensed API, and verify that automated and commercial use is permitted.

Should the tool return one AQI for all of Delhi NCR?

No. Delhi NCR has substantial spatial variation. Return a station-level or clearly defined area-level reading with station, distance, timestamp, and coverage limitations.

How fresh should AQI data be?

Follow the provider’s update cycle and define your own freshness thresholds. Always expose the observation timestamp so an agent can explain whether the reading is current or stale.

Can an AI agent send pollution alerts automatically?

Yes, but use a separate, authenticated alert tool with user confirmation, expiry, rate limits, and duplicate-event protection. Keep the read-only AQI tool free of side effects.

Apply for AI Grants India

If you are an Indian AI founder building a WebMCP air-quality agent, climate intelligence product, or public-impact application, apply to AI Grants India. Get support to turn a technically sound prototype into a responsible, scalable product.