Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Hopper MCP Server Should I buy flights from SFO to Tokyo now or wait for a price drop?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP (Model Context Protocol) server for Hopper — the AI-powered travel booking app with price prediction. This server enables AI assistants to search flights and hotels, get price forecasts, set alerts, and initiate bookings through Hopper's platform.
Flight search with real-time pricing and AI price predictions
Hotel search with ratings, amenities, and buy/wait recommendations
Price forecasting — buy now, wait, or watch recommendations with confidence %
Price alerts — get notified when prices hit your target
Flight & hotel booking — full booking flow initiation
Booking history — view current and past trips
Powered by patchright for stealth browser automation (bypasses bot detection).
Related MCP server: Duffel MCP Server
npm install -g @striderlabs/mcp-hopperOr run directly with npx:
npx @striderlabs/mcp-hopperAdd to your claude_desktop_config.json:
{
"mcpServers": {
"hopper": {
"command": "striderlabs-mcp-hopper"
}
}
}Config file locations:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
claude mcp add hopper striderlabs-mcp-hoppersearch_flightsSearch for available flights with Hopper's price predictions.
origin: "JFK"
destination: "LAX"
departure_date: "2025-06-15"
return_date: "2025-06-22" # optional, omit for one-way
passengers: 2 # default: 1
cabin_class: "economy" # economy | premium_economy | business | firstsearch_hotelsSearch for hotels with ratings, amenities, and price forecasts.
destination: "Paris"
check_in: "2025-06-15"
check_out: "2025-06-22"
guests: 2 # default: 2
rooms: 1 # default: 1
min_price: 100 # optional USD filter
max_price: 300 # optional USD filterget_price_forecastGet Hopper's AI buy/wait/watch recommendation with confidence percentage.
trip_type: "flight" # flight | hotel
origin: "NYC" # required for flights
destination: "Tokyo"
travel_date: "2025-08-01"
return_date: "2025-08-14" # optionalset_price_alertConfigure a price drop alert via Hopper app/email.
trip_type: "flight"
origin: "BOS"
destination: "LHR"
travel_date: "2025-07-20"
target_price: 450 # USD
email: "user@example.com"book_flightInitiate a flight booking with passenger and payment details.
flight_id: "flight_1" # from search_flights
passenger_first_name: "Jane"
passenger_last_name: "Smith"
passenger_email: "jane@example.com"
passenger_phone: "+1-555-0100"
payment_method: "credit_card"book_hotelInitiate a hotel booking. Hopper's Price Drop Guarantee refunds the difference if prices fall.
hotel_id: "hotel_2" # from search_hotels
check_in: "2025-06-15"
check_out: "2025-06-22"
guest_first_name: "Jane"
guest_last_name: "Smith"
guest_email: "jane@example.com"
payment_method: "credit_card"get_bookingsView current and past bookings from a Hopper account.
email: "jane@example.com"
booking_type: "all" # flight | hotel | all"Search for flights from New York to London in July, one week round trip"
"What's Hopper's price forecast for hotels in Barcelona next month?"
"Set a price alert for flights from SFO to Tokyo under $700"
"Book the cheapest flight you found for Jane Smith, email jane@example.com"
"Show me my past Hopper bookings for jane@example.com"
Booking and booking history features require a Hopper account. The server will guide users to authenticate at hopper.com when needed.
Price predictions use Hopper's displayed AI recommendations extracted from the live site.
For best results, use IATA airport codes (e.g. JFK, LHR, CDG) for flight searches.
git clone https://github.com/markswendsen-code/mcp-hopper
cd mcp-hopper
npm install
npm run build
node dist/index.jsMIT — Strider Labs hello@striderlabs.ai
Initiate a flight booking on Hopper for a specific flight. Requires passenger details and payment information. Returns booking confirmation or authentication requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| flight_id | Yes | Flight ID from search_flights results | |
| passenger_first_name | Yes | Passenger first name as on passport/ID | |
| passenger_last_name | Yes | Passenger last name as on passport/ID | |
| passenger_email | Yes | Passenger email for booking confirmation | |
| passenger_phone | Yes | Passenger phone number with country code | |
| payment_method | Yes | Payment method (e.g. 'credit_card', 'debit_card', 'paypal') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Adds valuable behavioral context by mentioning 'authentication requirements' as a possible return state, implying multi-step flow. However, misses critical safety context for a financial transaction: no mention of idempotency, payment failure handling, immediate vs. deferred charging, or reversibility.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste. Front-loaded with core action ('Initiate a flight booking...'), followed by prerequisites and return value. Every sentence earns its place; no redundant or filler text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for a 6-parameter booking tool with no output schema: compensates by describing return values ('booking confirmation or authentication requirements'). However, gaps remain for a financial transaction tool: missing error scenarios, pricing disclosure, cancellation policies, and explicit destructive/financial risk warnings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 100% description coverage, establishing baseline of 3. Description adds high-level grouping ('passenger details and payment information') but no additional semantic value regarding parameter relationships, validation rules, or format constraints beyond what the schema already documents.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Excellent specificity: 'Initiate a flight booking on Hopper for a specific flight' provides clear verb (initiate), resource (flight booking), platform (Hopper), and scope. Clearly distinguishes from sibling search_flights (booking vs. searching) and book_hotel (flight vs. hotel).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides prerequisite hints ('Requires passenger details and payment information') and expected outcomes ('Returns booking confirmation or authentication requirements'), but lacks explicit sequencing guidance (e.g., 'Use after search_flights') or exclusion criteria (when not to attempt booking).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Initiate a hotel booking on Hopper. Requires guest details and payment information. Hopper's Price Drop Guarantee refunds the difference if prices fall post-booking.
| Name | Required | Description | Default |
|---|---|---|---|
| hotel_id | Yes | Hotel ID from search_hotels results | |
| check_in | Yes | Check-in date in YYYY-MM-DD format | |
| check_out | Yes | Check-out date in YYYY-MM-DD format | |
| guest_first_name | Yes | Primary guest first name | |
| guest_last_name | Yes | Primary guest last name | |
| guest_email | Yes | Guest email for booking confirmation | |
| payment_method | Yes | Payment method (e.g. 'credit_card', 'debit_card') |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Adds valuable context about post-booking behavior (Price Drop Guarantee) and implies financial/sensitive data handling, but omits idempotency, cancellation policy, return value structure, or error states for this destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with clear front-loading of core action. Efficient structure, though the Price Drop Guarantee sentence provides platform context rather than tool behavior, slightly diluting focus.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequate for basic invocation but incomplete for a financial transaction tool: lacks description of return values (confirmation data, errors), auth requirements, or modification capabilities given zero annotations and no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, establishing baseline 3. Description adds semantic grouping ('guest details and payment information') but does not extend individual parameter semantics beyond what schema already documents (e.g., no format examples for payment_method).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States specific verb ('Initiate') + resource ('hotel booking') + platform ('Hopper'), clearly distinguishing from siblings like 'search_hotels' (query vs. transaction) and 'book_flight' (different resource).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies prerequisites via 'Requires guest details and payment information,' indicating when the tool is ready to use, but lacks explicit comparison to siblings (e.g., 'Use search_hotels first to obtain hotel_id').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
View current and past bookings from a Hopper account. Returns flight and hotel booking history with status, itinerary details, and pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address associated with the Hopper account | ||
| booking_type | No | Filter by booking type (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description carries burden of disclosing return structure—succeeds by specifying 'flight and hotel booking history with status, itinerary details, and pricing'. However, omits safety indicators (read-only/idempotent), rate limits, or authentication requirements beyond the email parameter.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficiently structured sentences: first states purpose, second discloses return payload. Zero redundancy despite lack of output schema—every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter retrieval tool without output schema, description adequately compensates by describing return contents (status, itinerary, pricing). Could improve by noting error conditions (e.g., invalid email) or pagination behavior, but sufficient for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for both 'email' and 'booking_type'. Description adds no parameter-specific guidance, but baseline 3 is appropriate since schema fully documents inputs without need for elaboration.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'View' with clear resource 'bookings from a Hopper account'. Distinctly positions this as a retrieval tool versus sibling actions like 'book_flight', 'book_hotel' (creation) and 'search_flights', 'search_hotels' (discovery).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage through 'current and past bookings' (history retrieval) versus booking new travel, but lacks explicit when-to-use guidance or prerequisites (e.g., 'use after booking confirmation' or 'requires active account').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get Hopper's AI price forecast for a flight or hotel. Returns whether to buy now, wait for lower prices, or watch for changes — with confidence percentage and reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| trip_type | Yes | Type of travel to forecast | |
| origin | No | IATA airport code for flight origin (required for flights) | |
| destination | Yes | Destination city or IATA code | |
| travel_date | Yes | Travel/check-in date in YYYY-MM-DD format | |
| return_date | No | Return/check-out date for round trips or hotel stays (YYYY-MM-DD) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Compensates by disclosing return value structure: categorical recommendation (buy/wait/watch), confidence percentage, and reasoning. Lacks operational details like rate limits or caching, but covers the critical behavioral trait of what the forecast contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. Front-loaded action ('Get...') followed immediately by return value specification. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 100% schema coverage and no output schema, description adequately compensates by explaining return values. Missing only forecast horizon/validity window details. Sufficient for agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are fully documented in structured fields. Description implicitly references trip_type via 'flight or hotel' but adds no semantic detail beyond schema (no examples, formats, or conditional logic explanations). Baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Get' + resource 'AI price forecast' + clear domain 'flight or hotel'. Distinguishes from siblings (search_flights, book_flight, set_price_alert) by emphasizing predictive analytics rather than search, booking, or alerts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage context ('whether to buy now, wait...') which signals this is for purchase timing decisions. However, lacks explicit when-not-to-use guidance or comparison to set_price_alert for ongoing monitoring vs one-time forecasts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for available flights on Hopper with real-time pricing and AI-powered price predictions. Returns flight options with buy/wait/watch recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| origin | Yes | IATA airport code (e.g. JFK, LAX, LHR) | |
| destination | Yes | IATA airport code (e.g. CDG, NRT, SYD) | |
| departure_date | Yes | Departure date in YYYY-MM-DD format | |
| return_date | No | Return date for round trips (YYYY-MM-DD). Omit for one-way. | |
| passengers | No | Number of adult passengers (default: 1) | |
| cabin_class | No | Cabin class (default: economy) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure burden. It successfully conveys behavioral traits: 'real-time pricing' indicates live data, 'AI-powered price predictions' discloses algorithmic processing, and 'buy/wait/watch recommendations' explains the decision-support output format. Missing only operational details like rate limits or no-results behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. First sentence front-loads core function and unique differentiators (AI predictions). Second sentence compensates for missing output schema by describing return structure. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema exists, the description appropriately describes return values ('flight options with buy/wait/watch recommendations'). With 6 well-documented parameters and high complexity, the description provides sufficient context for agent selection, though could mention one-way vs round-trip handling implied by optional return_date.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all 6 parameters fully documented (IATA formats, date syntax, enum values). The description correctly does not duplicate this information, maintaining baseline score. No additional semantic constraints (e.g., date range limits) are provided in description, but none are required given schema completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Search' with clear resource 'flights' and platform 'Hopper'. It distinguishes from sibling 'book_flight' by emphasizing this returns options rather than completing a transaction, and differs from 'search_hotels' via the flight-specific context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies workflow by stating it 'Returns flight options', suggesting this precedes booking, but lacks explicit when-to-use guidance versus 'get_price_forecast' or 'set_price_alert'. It establishes unique value (AI predictions, buy/wait/watch recommendations) but doesn't state exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for hotels on Hopper with price predictions. Returns hotel options with ratings, amenities, and Hopper's buy/wait recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| destination | Yes | City name or destination (e.g. Paris, New York, Tokyo) | |
| check_in | Yes | Check-in date in YYYY-MM-DD format | |
| check_out | Yes | Check-out date in YYYY-MM-DD format | |
| guests | No | Number of guests (default: 2) | |
| rooms | No | Number of rooms (default: 1) | |
| min_price | No | Minimum price per night in USD | |
| max_price | No | Maximum price per night in USD |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, description carries full burden. It effectively discloses return contents (ratings, amenities, buy/wait recommendation) since no output schema exists. However, fails to declare safety characteristics (read-only/non-destructive) or operational constraints like rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences with zero waste. First sentence establishes purpose and unique value prop (price predictions); second sentence details return value. Perfectly front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Appropriately complete for a 7-parameter search tool with full schema coverage. Compensates for missing output schema by detailing return fields. Minor gap: lacks explicit read-only declaration expected in absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage with clear examples (date formats, defaults). Description adds no additional parameter-specific semantics, but baseline 3 is appropriate given schema completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Search' and resource 'hotels on Hopper'. Distinguishes from generic search via 'price predictions' and specific platform mention. Lacks explicit distinction from sibling 'book_hotel' (browse vs purchase workflow), preventing a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides no guidance on when to use this tool versus siblings like 'book_hotel' (search first, then book) or 'get_price_forecast' (direct forecast vs hotel listing with forecast). No prerequisites or exclusions stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a price drop alert for a flight or hotel. Hopper will notify via the app/email when prices hit your target or when the optimal booking window opens.
| Name | Required | Description | Default |
|---|---|---|---|
| trip_type | Yes | Type of travel to watch | |
| origin | No | IATA airport code for flight origin | |
| destination | Yes | Destination city or IATA code | |
| travel_date | Yes | Travel/check-in date in YYYY-MM-DD format | |
| target_price | Yes | Alert threshold price in USD | |
| Yes | Email address to send price alerts to |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and successfully explains the asynchronous notification behavior ('Hopper will notify via the app/email when...'). It clarifies trigger conditions (target price hit or optimal window opening) but omits alert persistence duration, cancellation methods, and authentication requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences with zero waste: first states the action and scope, second explains the behavioral outcome. Information is front-loaded with the core purpose immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the straightforward parameter schema (flat structure, 100% coverage) and lack of output schema, the description adequately covers the core user journey. It could improve by noting the optional nature of 'origin' for flight alerts or what the tool returns upon creation, but it sufficiently explains the value proposition and operational flow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, establishing a baseline of 3. The description adds context by mentioning 'Hopper' and the notification mechanism but does not expand on parameter semantics beyond what the schema already provides (e.g., doesn't clarify that 'origin' is optional despite being a flight parameter, or explain the IATA code format constraints).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb ('Create') and clear resource ('price drop alert'), explicitly scoping to 'flight or hotel' which distinguishes it from sibling booking tools (book_flight, book_hotel) and search tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
While the description explains the tool's function, it lacks explicit guidance on when to use this versus immediate booking (book_flight/hotel) or checking forecasts (get_price_forecast). Usage is implied ('wait for price drops') but no prerequisites or alternative comparisons are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0book_flightbook_hotelget_bookingsget_price_forecastsearch_flightssearch_hotelsset_price_alertEach tool has a distinct purpose: booking flights/hotels, retrieving bookings, forecasting prices, searching flights/hotels, and setting alerts. No overlap exists; for example, search_flights and book_flight are clearly separate operations, and get_price_forecast focuses on predictions rather than actions.
All tool names follow a consistent verb_noun pattern (e.g., book_flight, search_hotels, set_price_alert). The verbs are descriptive and aligned with the actions, and there are no deviations in style or convention across the set.
With 7 tools, the server is well-scoped for travel booking and price management. Each tool serves a clear function in the workflow, from search and forecast to booking and alerts, without being overly sparse or bloated.
The tool set provides complete coverage for the travel booking domain: search (flights/hotels), forecast (prices), booking (flights/hotels), management (view bookings), and alerts (price drops). No obvious gaps exist, as it supports the full lifecycle from research to post-booking monitoring.
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