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wolt-mcp

by fogside
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Python
Remote

wolt-mcp

A thin Model Context Protocol server that exposes Wolt's public consumer endpoints to AI agents. Point it anywhere Wolt operates — the default coordinates are Tallinn, Estonia, but you can override per-call or via env vars.

Two tools, no magic:

  • list_nearby — venues near a lat/lon, filtered by substring, rating, open/closed status.

  • get_menu — full menu for a venue by slug, with prices, 30-day lows, and category structure.

It's read-only on purpose. Wolt's ordering API is gated behind merchant credentials; this server intentionally doesn't try to place orders.

Who this is for

You want an AI assistant (Claude Code, Cursor, Continue, Zed, or your own Agent SDK app) that can:

  • Discover restaurants by cuisine, rating, or open status in your city.

  • Read full menus with live prices into its context window.

  • Watch favorite venues for deals — discount categories like ERIPAKKUMISED (Estonian), SPECIAL OFFERS, or KUUPAKKUMINE (monthly offer), plus lowest_price (the 30-day low used for EU compliance display).

  • Draft weekly meal plans by composing items across a curated shortlist of venues.

Related MCP server: OrderFood MCP

Geographic coverage

Wolt's consumer-api.wolt.com is global; this server has no country hardcoding. Change WOLT_DEFAULT_LAT / WOLT_DEFAULT_LON (or pass lat/lon per call) to work anywhere Wolt delivers — Finland, Germany, Czechia, Estonia, Israel, Greece, Japan, and more. Verified live against Tallinn (59.4370, 24.7536); other regions should work with the same endpoints.

Install

git clone https://github.com/fogside/wolt-mcp
cd wolt-mcp
python3 -m venv .venv
.venv/bin/pip install -e .

Then register it with any MCP-capable client. For Claude Code, drop this at the project root:

{
  "mcpServers": {
    "wolt": {
      "command": "./.venv/bin/wolt-mcp",
      "args": [],
      "env": {
        "WOLT_DEFAULT_LAT": "59.4370",
        "WOLT_DEFAULT_LON": "24.7536",
        "WOLT_LANGUAGE": "en"
      }
    }
  }
}

For Claude Desktop, add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or the equivalent on Linux/Windows:

{
  "mcpServers": {
    "wolt": {
      "command": "/absolute/path/to/wolt-mcp/.venv/bin/wolt-mcp",
      "env": { "WOLT_DEFAULT_LAT": "59.4370", "WOLT_DEFAULT_LON": "24.7536" }
    }
  }
}

Any other MCP client that supports stdio transport works the same way — run wolt-mcp as the command.

Tools

list_nearby

Param

Type

Default

Notes

lat

float

$WOLT_DEFAULT_LAT

lon

float

$WOLT_DEFAULT_LON

radius

int (m)

3000

200–20000

query

str?

None

Substring match against name + tags

only_open

bool

False

min_rating

float?

None

0–10 scale

max_results

int

30

language

str

$WOLT_LANGUAGE or en

Returns a list of dicts: name, slug, id, online, rating, rating_volume, eta_minutes, price_range, tags, short_description, address.

get_menu

Param

Type

Default

Notes

slug

str

—

From list_nearby, e.g. vapiano-foorum

language

str

en

include_disabled

bool

False

Returns { slug, assortment_id, primary_language, selected_language, available_languages, categories: [{ id, name, slug, description, items: [...] }], uncategorised_items, item_count }. Each item has id, name, description, price, original_price, lowest_price, enabled, tags.

Prices are integers in minor units. 2390 = €23.90. Currency is not on items — infer from venue country.

Environment variables

Var

Default

Purpose

WOLT_DEFAULT_LAT

59.4370

Tallinn center

WOLT_DEFAULT_LON

24.7536

WOLT_LANGUAGE

en

Sent as Accept-Language

WOLT_MCP_LOG

WARNING

Python logging level

Example conversations

"Find the top five sushi places near me that are open right now." → list_nearby(query="sushi", only_open=True, min_rating=9, max_results=5)

"Pull Vapiano Foorum's menu and tell me what's vegetarian." → get_menu(slug="vapiano-foorum") then the LLM filters by description.

"Compare prices for pad thai between these three Thai venues." → one list_nearby + three get_menu calls.

"Summarise current deals at my favorite café." → get_menu — surface items in categories named like ERIPAKKUMISED / SPECIAL OFFERS / KUUPAKKUMINE, or with ⭐ in the name.

Detecting deals — an important gotcha

Wolt venues in practice use category membership as their deal signal much more often than original_price. A typical Estonian cafe will have a category named ERIPAKKUMISED ("special offers") or KUUPAKKUMINE ("monthly offer") containing the discounted items, while original_price on those items stays null.

When prompting your agent, don't rely on a price-diff — ask it to inspect category names and item names (⭐ prefixes are common) as the first-class signal. lowest_price is useful for longer-range price-tracking: it's the 30-day low that Wolt surfaces for EU price-transparency compliance.

Non-goals and limitations

  • No ordering, cart, or checkout. Wolt's consumer cart/order flow requires authenticated user sessions + isn't in this MCP's scope. For commerce, use Wolt's merchant API (partner credentials required).

  • No user-account actions. Can't read your order history or favorites.

  • Language. The assortment endpoint often returns the venue's primary language (e.g. Estonian) even when en is requested — most venues don't publish auto-translated content. You'll see the actual selected language in the response's selected_language field.

  • Rate limits. Wolt returns 429s under aggressive use. For personal use this doesn't matter; for anything heavier, add client-side pacing.

Terms of service

This project hits Wolt's public, unauthenticated consumer endpoints — the same ones wolt.com's web app calls. Automated access at scale is contrary to Wolt's Terms of Service. Use this for personal agent assistance, experimentation, or research. Don't build a scraper at scale with it.

Credits and sources of inspiration

Development

.venv/bin/pip install -e ".[dev]"  # once dev extras are defined
.venv/bin/wolt-mcp                 # runs the server on stdio — connect a client

Contributions welcome — especially venue-specific deal-detection heuristics for cities outside Estonia, or a venue_dynamic(slug) tool if you can find a working endpoint.

License

MIT.

Available Tools

2 tools
get_menuA

Fetch a venue's full menu by slug. Returns categories and items with prices in minor units (e.g. 2390 = 23.90 EUR in Estonia). Each item includes price, original_price (if discounted), lowest_price (30-day low used for compliance), description, enabled, and tags. A category named 'ERIPAKKUMISED' (Estonian) or similar typically holds current deals.

ParametersJSON Schema
NameRequiredDescriptionDefault
slugYesVenue slug, e.g. 'vapiano-foorum'
languageNoPreferred language code, e.g. 'en', 'et'en
include_disabledNoInclude items marked disabled/out-of-stock

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description discloses return format details (categories, items, price fields in minor units, special category for deals). It adds behavioral context beyond schema, though it omits potential side effects or auth requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient paragraph that front-loads the main action. It is concise with no redundancy, though structure could be slightly improved with bullet points for clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (implied) and 3 parameters, the description covers essential return fields and a special note about deal categories. It is complete for effective tool usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already describes all 3 parameters. The description only re-emphasizes the slug with an example, adding negligible value beyond structured metadata.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Fetch a venue's full menu by slug'), specifies the resource (venue menu), and distinguishes from the sibling tool list_nearby which likely deals with nearby venues rather than menu details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives (e.g., list_nearby) or when not to use it. It only explains what the tool does without contextual usage cues.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_nearbyA

List Wolt venues near a location. Defaults to Tallinn center. Returns compact dicts with slug, rating, ETA, online status, tags. Use query to substring-match name/tags. Prices are not included here — call get_menu for a specific slug.

ParametersJSON Schema
NameRequiredDescriptionDefault
latNoLatitude
lonNoLongitude
radiusNoSearch radius in meters
queryNoCase-insensitive substring match against venue name and tags
only_openNoFilter to currently-online venues
min_ratingNoMinimum rating (0–10 scale)
max_resultsNoMax venues to return
languageNoWolt response language, e.g. 'en', 'et'en

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Despite no annotations, the description discloses what the tool returns (compact dicts with slug, rating, ETA, online status, tags), what it does not return (prices), and the default behavior (Tallinn center). It does not cover all edge cases but is transparent enough for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two dense sentences pack purpose, defaults, return format, query usage, and sibling guidance with zero wasted words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 optional parameters, no required params, and no output schema, the description effectively explains the return format, filtering options, and when to use the sibling tool. It is complete enough for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the input schema already describes each parameter well. The description adds marginal value by reaffirming the default location and the substring-match behavior of `query`, but does not provide significant new insight beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'List' and resource 'Wolt venues near a location', specifies the default location (Tallinn center), and distinguishes itself from the sibling tool 'get_menu' by noting that prices are not included here.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit guidance on when to use this tool versus the alternative: 'Prices are not included here — call get_menu for a specific slug.' It also explains how to use the `query` parameter for substring matching, and mentions defaults and filtering options.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv0.1.0
    • First observedget_menu
    • First observedlist_nearby
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TDQS

A3.9/5.0
Disambiguation5/5

The two tools have completely distinct purposes: list_nearby finds venues, get_menu retrieves menu details for a specific venue. No overlap.

Naming Consistency4/5

Both tools follow a verb_noun pattern ('get_menu', 'list_nearby'), though 'list_nearby' uses an adjective which is a minor deviation.

Tool Count3/5

Only 2 tools is on the low end for a food delivery service, but it may be acceptable for a simple discovery-focused server. However, it feels thin for broader use cases.

Completeness2/5

The server covers basic venue discovery and menu retrieval, but lacks essential operations like searching items, user authentication, order management, or venue details beyond menus.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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