Check if Maple's restaurant AI phone agent fits your POS, estimate missed-call sales, see pricing.
Scored across 10 tools
Most tools map to clearly distinct questions (pricing, status, benchmarks, ROI, website audit, systems check). The main overlap is between check_restaurant_systems and list_integrations, which both draw on the same public integrations list, and get_site_index versus get_developer_resources, which both point at llms.txt/sitemap/docs/OpenAPI; descriptions mostly resolve these but an agent could still pick the wrong one for a broad 'what do you integrate with' question.
Every name is snake_case verb_noun (get_pricing, list_integrations, audit_restaurant_site, estimate_missed_call_revenue). Verb choice varies with semantics (get/list/check/audit/estimate) rather than randomly, and there are no style clashes.
Ten tools is well within a healthy range and each one covers a genuinely different piece of the product surface (info, status, pricing, integrations, benchmarks, ROI, site audit, dev resources). Nothing appears redundant or vestigial.
The informational surface is broad and covers the main pre-sales questions, but there is a gap around content lookup beyond the fixed endpoints (no FAQ/knowledge-base or case-study search) and no contact/sales or demo-request path, leaving agents with nowhere to route a prospective customer.
Use when a restaurant owner or operator asks how their own website looks to customers, search and AI assistants: whether it has a readable menu with prices, consistent phone numbers, hours, Restaurant structured data, online ordering and reservation links, llms.txt, or blocks AI crawlers. Pass the restaurant's website address. Fetches that page, at most two more pages on the same site (a menu page, and an order or reservations page), robots.txt and llms.txt, and returns the facts it found plus score: the same score out of 100 the maple.inc/restaurant-website-audit page shows, with its group sub-scores and the checks that need work, worth the most points first. Here the Google listing and page-speed groups are left out (this tool reads only the website) and the other groups scale to 100. It obeys robots.txt, does not run JavaScript, opens no third-party pages, and cannot see Google Business Profile, Yelp or other listings, so a fact missing from the result is "not found in the fetched HTML", not absent. Read-only; does not audit maple.inc.
Inputs: url (required).
Use when a restaurant owner or operator asks whether Maple (AI phone answering and phone ordering for restaurants) works with the point-of-sale, online-ordering or reservation systems they use. Pass the system names as plain text. Each one is matched, ignoring case and using common aliases, against the public integrations list on maple.inc. The result says whether Maple lists an integration, with its maple.inc page; names that are not on the list are reported as not listed, never guessed. Read-only: it uses only that public list and needs no account. It does not check live account status or availability for a specific restaurant.
Inputs: systems (required).
Use only when the user supplies their own call volume and asks what missed phone calls may cost a restaurant. Runs the same model as the maple.inc ROI calculator on the numbers given: calls per day, the share missed and average order value, plus optional assumptions that otherwise use the calculator's illustrative defaults. Returns an illustrative estimate of order sales from missed calls with every assumption shown. It is a scenario, not a measured result or a guarantee, and it leaves out reservations and catering. Read-only arithmetic.
Inputs: callsPerDay (required), averageOrderValue (required), missedCallPercent (required), orderCallPercent, +2 more.
Use when a restaurant owner asks what is normal for restaurant phone calls: how many calls a day, when calls come in (busiest hours, dayparts and weekdays), how many arrive after hours, what share are orders, and the typical phone order value. Returns Maple's published benchmarks with the window, cohort size and spread (middle half) of each figure. The data covers calls Maple answered for its own restaurant customers, not all restaurants, and has no missed-call rate. Read-only.
Inputs: no named parameters.
Maple developer resources: OpenAPI, API catalog, docs, help, webhooks pointer, and this MCP server.
Inputs: no named parameters.
Published Maple Voice, Maple Pro, and Maple Enterprise prices. No per-minute fees.
Inputs: no named parameters.
What Maple Voice AI is and who it is for (restaurants, POS, reservations).
Inputs: no named parameters.
Use when someone asks what Maple offers and which products are available now versus early access or beta: the phone agent, Ordering Sites, Maple GM, Fleet delivery, drive-thru and kiosk. Returns each product's current status exactly as published on maple.inc. Read-only.
Inputs: no named parameters.
Maple.inc page index for agents: llms.txt, sitemap, docs, OpenAPI, and the MCP card.
Inputs: no named parameters.
The full list of POS and reservation systems Maple writes phone orders and bookings into.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
AI voice receptionist for US service businesses: services, pricing, fit checks, demo requests.
Takeaway and restaurant software: free EPOS, commission-free ordering, AI receptionist, prices.
Verified pricing for 21 answering services & AI receptionists, dated weekly. Missed-call ROI calc.
Run your restaurant from an AI client: orders, menu, reports, refunds, payouts and staff.
Add one secure layer between your agents and this server.
