Melo

@melo show me all my saved searches and their current status

Here are your 3 active searches in Melo: 1. **Senior Backend Engineer - Berlin** (created 2 days ago): 47 new matches since last check, monitoring 12 job boards 2. **Product Manager - Remote EU** (created 1 week ago): 23 new matches, monitoring 8 job boards 3. **DevOps Lead - Amsterdam** (created 3 weeks ago): 15 new matches, monitoring 10 job boards All searches are running with daily refresh cycles. The Berlin search has the highest match velocity right now.

This example shows how the MCP surfaces your existing search configurations and their activity. Requires a valid Melo API key with read access to your account's searches. The match counts and board coverage reflect real-time state, so numbers will vary by when you last synced.

@melo I'm looking for jobs in 'San Fran' — what locations does that match?

Melo's location autocomplete found these matches for 'San Fran': 1. **San Francisco, California, United States** (city) 2. **San Francisco Bay Area, California, United States** (metro area) 3. **San Francisco County, California, United States** (county) The first option (city proper) is most specific. If you're open to surrounding areas like Oakland or San Jose, choose the Bay Area option. I can help you create a search using any of these location filters.

Demonstrates how the MCP resolves ambiguous location queries before you commit to a search configuration. Useful when setting up new job searches to ensure you're targeting the right geographic scope. The autocomplete supports country and location-type filters if you need to narrow results further.

@melo simulate a webhook event for my staging endpoint to test the integration before I go live

Webhook simulation sent to your staging endpoint: **Endpoint:** `https://staging.yourapp.com/webhooks/melo` **Event Type:** `search.new_matches` **Payload:** Sample event with 3 mock job matches for a Berlin-based search **Response:** 200 OK (received in 340ms) Your endpoint successfully acknowledged the test event. The payload structure matches what you'll receive in production when new job matches arrive. You can now enable live webhooks with confidence that your handler is working.

This example shows the MCP's webhook testing capability, which lets you validate your integration without waiting for real job-match events. Requires your Melo account to have a configured webhook endpoint. The simulation uses realistic payload structure but synthetic data, so you can safely test error handling and parsing logic.

When Melo handles location normalization for property data

A 3-person proptech startup ingests rental listings from 12 scrapers that format addresses inconsistently. Melo's location autocomplete and city lookup tools standardize this mess before the data hits their database. The team runs a nightly batch job that calls the autocomplete endpoint for each raw address string, then validates the returned city against Melo's canonical list. This works cleanly up to about 5,000 listings per night—beyond that, you'll want to cache city lookups or move to a bulk geocoding service. The webhook simulator is useful during initial integration testing, but most teams stop using it after the first week. If your address data is already clean or you're under 500 records, skip Melo and use a free geocoding API. For messy multi-source location data at small scale, Melo's normalization is worth the API key.

Melo for triaging support requests to local reps

A 6-person SaaS support team routes tickets to regional reps based on customer location. When a ticket arrives with a city name or partial address, the team's Switchy workspace calls Melo's location autocomplete to resolve it to a canonical city, then matches that city to a rep's coverage area. This setup works because Melo returns structured location data (city, country, type) that maps cleanly to the team's routing rules. The list searches tool lets the team audit which location queries have been run, useful for spotting patterns in misspelled city names or ambiguous queries. If your ticket volume is under 200 per day and you need sub-country routing, Melo handles it without custom geocoding logic. Above that volume or if you only route by country, a simpler IP geolocation service is faster and cheaper.

When Melo's city filters speed up venue research

A 2-person event agency researches venues and vendors across 30 US cities for corporate clients. They use Melo's get cities tool with country and name filters to quickly pull lists of cities in a target region, then feed those city names into their vendor database queries. This eliminates the manual step of typing city names or copying from a spreadsheet. The location autocomplete tool helps when a client mentions a neighborhood or metro area instead of an exact city—Melo resolves it to the canonical city name the vendor database expects. The workflow saves about 15 minutes per event proposal. If your research spans fewer than 10 cities or you already have a static city list, Melo is overkill. For agencies doing frequent multi-city research with varied location inputs, the autocomplete and filter combo justifies the API key and four-tool overhead.