Yelp MCP Server
Provides read-only tools for searching Yelp businesses by keyword and location, retrieving full business details such as hours, phone, website, features, and menu information, and paging through complete review feeds, all as structured JSON.
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., "@Yelp MCP ServerFind top-rated sushi spots in Chicago and show their latest reviews."
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.
Yelp MCP Server
A hosted Model Context Protocol (MCP) server that gives Claude, Cursor, Windsurf and any other MCP client three read-only Yelp tools. Search businesses by keyword and location, read one business in full, and page through its complete review feed, all as structured JSON, with no Yelp Fusion key and nothing to host.
It reads public Yelp pages that a signed-out visitor can see, on any of the 41 regional domains.
1,000 free credits every month, no card required, which is 100 Yelp calls at the 10-credit rate.
https://mcp.hasdata.com/api/mcp?apis=yelp
Contents
Related MCP server: Yelp Fusion MCP Server
What you need
An MCP client and a HasData API key from the dashboard, free to create with no card, and the free tier covers about 100 calls a month at the 10-credit rate. This is a remote server, so the simplest path is a URL and an x-api-key header, with no container to run. A client that only speaks stdio reaches it through a thin launcher, published as @hasdata/yelp-mcp on npm and hasdata-yelp-mcp on PyPI, shown below.
Quick start
The server URL is the same for every client. We run it hands-on in Claude Code and Claude Desktop. The other blocks follow each client's own documented format for a remote server.
Field | Value |
URL |
|
Transport | HTTP, streamable |
Auth header |
|
Clients with OAuth support can add the same URL as a connector and sign in without putting a key in a config file.
claude mcp add --transport http yelp "https://mcp.hasdata.com/api/mcp?apis=yelp" \
--header "x-api-key: HASDATA_API_KEY"Settings, then Connectors, then Add custom connector, then paste https://mcp.hasdata.com/api/mcp?apis=yelp and sign in.
For the config-file route, Claude Desktop loads only local (stdio) servers, so it reaches a remote server through a stdio launcher. The @hasdata/yelp-mcp package is that launcher, and it reads the key from the environment. Add this to claude_desktop_config.json:
{
"mcpServers": {
"yelp": {
"command": "npx",
"args": ["-y", "@hasdata/yelp-mcp"],
"env": { "HASDATA_API_KEY": "YOUR_KEY" }
}
}
}For Python instead of Node, swap the launcher for the PyPI package, which uvx runs without a manual install:
{
"mcpServers": {
"yelp": {
"command": "uvx",
"args": ["hasdata-yelp-mcp"],
"env": { "HASDATA_API_KEY": "YOUR_KEY" }
}
}
}~/.cursor/mcp.json for every project, or .cursor/mcp.json for one:
{
"mcpServers": {
"yelp": {
"url": "https://mcp.hasdata.com/api/mcp?apis=yelp",
"headers": { "x-api-key": "HASDATA_API_KEY" }
}
}
}~/.codeium/windsurf/mcp_config.json. Windsurf calls the field serverUrl, not url:
{
"mcpServers": {
"yelp": {
"serverUrl": "https://mcp.hasdata.com/api/mcp?apis=yelp",
"headers": { "x-api-key": "HASDATA_API_KEY" }
}
}
}.vscode/mcp.json in the workspace:
{
"servers": {
"yelp": {
"type": "http",
"url": "https://mcp.hasdata.com/api/mcp?apis=yelp",
"headers": { "x-api-key": "HASDATA_API_KEY" }
}
}
}Example prompts
Each of these lands on one tool, or on two in sequence when the second needs an identifier the first returns.
Find coffee roasteries in Austin, TX and rank them by rating against review count.
Pull the hours, phone and website for the Yelp business
desnudo-coffee-austin.Read every one-star and two-star review of this business and group the complaints by theme.
Which dishes does Yelp list as popular for this place, and what do reviewers say about them?
Compare the rating distribution of these three competitors in the same neighborhood.
Show me the reviews Yelp does not recommend for this business and how they differ from the recommended ones.
A prompt that names a business rather than a Yelp ID takes two calls, one search to resolve the ID and one place or reviews lookup to read it. The search tool returns both placeId and placeAlias, and either one works as the placeId argument to the place tool.
Tools
Three tools, 10 credits per successful call. Every tool accepts domain to switch country, one of 41 values, from www.yelp.com through the European, Asian and Latin American sites, including the language-specific variants such as fr.yelp.ca and zh.yelp.com.hk.
Get Yelp search results
hasdata_yelp_search_getSearchResults
A page of businesses for a keyword in a place.
Parameter | Type | Required | Notes |
| string | yes | What to search for, such as |
| string | yes | Where to search, such as |
| string | Map bounding box instead of a radius, as | |
| string | Yelp site, defaults to | |
| number | Result offset, stepping by 10 |
Returns searchInformation with the echoed keyword, location and totalResults, an ads array of paid placements, an organicResults array, and pagination with currentPage, perPage, totalPages, nextPageUrl and otherPagesUrls.
The two result arrays are not the same shape. An organic result carries position and streetAddress, while an ad carries neither and adds phone and a highlights array of the badges Yelp shows advertisers. Merging the arrays without checking which one a business came from turns paid placement into rank.
{
"position": 2,
"placeId": "oiJ7QuhhpsEpe9zFTZF0bA",
"placeAlias": "desnudo-coffee-austin",
"url": "https://www.yelp.com/biz/desnudo-coffee-austin",
"title": "Desnudo Coffee",
"streetAddress": "2505 Webberville Rd, Austin",
"price": "$$",
"categories": [{ "title": "Coffee Roasteries", "url": "https://www.yelp.com/search?find_desc=Coffee+Roasteries&find_loc=Austin%2C+TX" }],
"snippet": "My favorite coffee shop to go to ever! Everyone must go. [[HIGHLIGHT]]Great coffee[[ENDHIGHLIGHT]], great vibes,great customer...",
"rating": 4.7,
"reviews": 347,
"thumbnail": "https://s3-media0.fl.yelpcdn.com/bphoto/0nE_IcbRiyxrw3kk2z6owg/ls.jpg",
"allImagesUrl": "https://www.yelp.com/biz_photos/oiJ7QuhhpsEpe9zFTZF0bA"
}Get Yelp place details
hasdata_yelp_place_getPlaceDetails
One business in full.
Parameter | Type | Required | Notes |
| string | yes | A Yelp ID such as |
| string | Yelp site, defaults to |
Returns a placeResult object with name, url, address, neighborhoods, country, phone, website, price, categories, rating, reviews, isClaimed and isClaimable, an operationHours object holding a week of hours plus today, a features array, a menu object with popularDishes, a faqs array of questions Yelp readers asked and answered, a reviewHighlights array of the phrases Yelp pins to the top of the page, an images array, and businessMap, a static map image URL.
features is the amenities block, and each entry carries a title and an isActive flag, so a false entry is Yelp stating the amenity is absent rather than unknown. That difference matters when you filter, because dropping the false entries and dropping the missing ones are not the same query.
{
"name": "Desnudo Coffee",
"url": "https://www.yelp.com/biz/desnudo-coffee-austin",
"address": "2505 Webberville Rd Austin, TX 78702",
"neighborhoods": "East Austin",
"country": "US",
"phone": "(424) 400-1857",
"website": "http://www.desnudocoffee.com",
"price": "$$",
"categories": ["Coffee Roasteries"],
"rating": 4.7,
"reviews": 347,
"isClaimed": true,
"isClaimable": false,
"operationHours": { "hours": [{ "day": "Mon", "hours": ["7:00 AM - 2:00 PM"] }] },
"features": [
{ "title": "Offers delivery", "isActive": true },
{ "title": "ADA-compliant restroom", "isActive": false }
],
"menu": {
"section": "Popular Drinks",
"popularDishes": [{ "name": "Brown Sugar Miso Latte", "rating": 4.7, "reviews": 113, "photos": 71 }]
}
}Get Yelp place reviews
hasdata_yelp_reviews_getPlaceReviews
The review feed of one business, in full text, with sorting, filtering and pagination.
Parameter | Type | Required | Notes |
| string | yes | The Yelp ID of the business |
| string | Yelp site, defaults to | |
| string |
| |
| string | Keep only these star ratings, such as | |
| string | Free-text search inside the reviews | |
| string | Two-letter language of the reviews, defaults to | |
| boolean | Return the feed Yelp filters out instead of the recommended one | |
| number | Offset, stepping by | |
| number | Page size, 49 at most, and 49 by default | |
| string | Cursor taken verbatim from the previous response |
Returns searchInformation with the business name, alias, URL, totalResults, rating, a reviewCountsByRating array and a reviewCountsByLanguage breakdown, a pagination object, and a reviews array.
Each review carries position, id, link, a user object, a comment object holding text and its detected language, date, rating, and, when the reviewer attached them, photos, videos and reactions. The user object reports name, userId, address, lifetime reviews, friends and photos counts, and eliteYear for a Yelp Elite member.
A review the author later rewrote also carries previousReviews, holding the earlier version with its own text, rating and date. That is the field to read when the question is whether a rating moved, because the current review alone cannot answer it.
{
"position": 1,
"id": "7zLIm3c2v2hRBVmgaKkR7w",
"link": "https://www.yelp.com/biz/desnudo-coffee-austin?hrid=7zLIm3c2v2hRBVmgaKkR7w",
"user": {
"name": "Karson S.",
"userId": "3LxSs_dQ37-LBRz07EDbyg",
"address": "Austin, TX",
"reviews": 374,
"friends": 61,
"photos": 875,
"eliteYear": "26"
},
"comment": { "text": "Coming back to Desnudo to update my old review...", "language": "en" },
"date": "2026-08-20T17:33:47-05:00",
"rating": 5,
"photos": [{ "link": "https://s3-media0.fl.yelpcdn.com/bphoto/YbjFKmRf7j0ejQQSaqtqVw/o.jpg", "caption": "Matcha latte", "width": 1126, "height": 2000 }],
"reactions": [{ "type": "HELPFUL", "label": "Helpful", "count": 1 }],
"previousReviews": [{ "id": "0WNI2IG7K1_Dg9zdXm03SA", "rating": 4, "comment": { "text": "..." } }]
}Errors and failure paths
Plan for these rather than assuming a happy path.
A search with no matches returns a successful result with an empty organicResults array, not an error. requestMetadata.status is still ok. Test the array length before iterating.
snippet is marked-up text, not clean text. Yelp wraps the matched words in [[HIGHLIGHT]] and [[ENDHIGHLIGHT]], and those markers arrive verbatim. Strip them before you index, embed or display the snippet.
Category URLs arrive HTML-escaped. The url inside a categories entry contains & rather than a bare ampersand, because that is how it sits in the page. Unescape it before following the link.
rating and query do not combine on the reviews tool. Yelp ignores the star filter while a free-text query is running, so a filtered search comes back with reviews of every rating. Filter the result yourself when you need both.
start and nextPageToken are two different ways to page, and they do not mix. Pass one or the other. The token carries both the offset and the page size, so resending it alone continues the feed, while start needs num to stay put across calls.
The not-recommended feed is a different feed with different limits. Setting notRecommended returns reviews Yelp filtered out of the main list, and those come ten at a time rather than 49, with no photos, videos or reactions attached.
A business can be unclaimed, and an unclaimed page is thin. isClaimed false usually means no website, no hours and no amenities, because nobody filled them in. Read the flag before you treat a missing field as a scraping failure.
Results that carry data also carry a requestMetadata.id worth quoting in support.
Pricing, free tier and limits
Each Yelp tool costs 10 credits per successful call. Response size does not change the price, so a 49-review page and a 5-review page cost the same, which makes the largest page the cheapest way to read a feed.
The free tier is 1,000 credits every month with no card, which is 100 Yelp calls at the base rate. It renews with the billing cycle, so a low-volume agent runs on the free tier indefinitely.
Paid plans start at $49 a month for 200,000 credits, which is 20,000 calls. The unit price falls with volume, from $2.45 per 1,000 calls on the entry plan to $1.00 on Business, $0.84 on Growth and $0.74 on the largest high-volume plans.
Your plan also sets concurrency. The free tier allows 1 request at a time, Startup 15, Business 30, Growth 50, and the high-volume plans run from 200 to 1,500. Handle the overflow case defensively in anything unattended, because an agent that fans out across a list of businesses will reach the ceiling before you do.
A request that comes back non-200 is not billed. A successful call that finds nothing is still a call.
Tool selection
Start from what the prompt gives you. A keyword and a place go to the search tool, a Yelp ID or alias goes straight to the place tool, and a question about what customers said goes to the reviews tool. Spending a search call to reach an ID you already have is the most common waste.
Then pick by what the question is about. The place tool answers questions about the business itself, its hours, amenities, price tier and headline rating. The reviews tool answers questions about its customers, and it is the only one that returns review text, authors and the rating distribution. The reviewHighlights block on the place tool is a sample Yelp curates, not a substitute for the feed.
Read the feed with the largest page. num defaults to its maximum of 49 already, so leave it alone unless you are deliberately sampling.
How it compares
Yelp's own Fusion API is the official route to this data, and it is a different instrument.
Yelp Fusion API | This server | |
Eligibility | An approved developer app | An API key |
Review text | Up to three per business, truncated | The full feed, full text, paged |
Review authors | Name and photo | Name, location, lifetime counts, Elite year |
Rating distribution | Not returned |
|
Filtered reviews | Not returned | The not-recommended feed |
Edit history | Not returned |
|
Amenities and hours | A limited attribute set | The amenities block as the page shows it |
The row that decides it is review text. Fusion returns three excerpts per business, which answers a display question on a storefront and cannot answer an analysis question about sentiment, complaints or how a rating moved. When three excerpts and an official contract are what you need, Fusion is the better fit.
FAQ
Is there an official Yelp MCP server?
Yelp does not publish one. This one is maintained by HasData and reads public Yelp pages.
What is a Yelp MCP server?
An MCP server exposes tools an AI client can call. This one turns Yelp search results, business pages and review feeds into JSON an agent can reason over, without a browser or a scraping library in your stack.
Do I need a Yelp account or a Fusion key?
No. The only credential is your HasData key.
Which Yelp sites are covered?
All 41 domains the API accepts, from www.yelp.com through the European, Asian and Latin American sites. Several countries have more than one, split by language, such as fr.yelp.ca next to www.yelp.ca. Pass domain to switch.
Can I get every review of a business?
Yes, by paging. The feed returns 49 at a time, and pagination.hasNextPage tells you when to stop. Reading a business with 347 reviews takes eight calls.
What is the difference between recommended and not-recommended reviews?
Yelp runs software that hides some reviews from the main feed. The default response is the recommended feed, the one a visitor sees. Setting notRecommended returns the hidden one instead, which is smaller, paged ten at a time, and stripped of photos and reactions.
Why did my rating filter return every rating?
Because a query was set at the same time. Yelp drops the star filter when it runs a text search, so the two cannot be combined server-side.
Can I use this together with other HasData APIs?
Yes. One key covers everything, and one endpoint serves them all through the apis parameter. Point a client at ?apis=yelp,google_maps to get both tool sets in one connection, or at mcp.hasdata.com/api/mcp for the full catalogue.
Is HasData affiliated with Yelp?
No. HasData is an independent service and is not affiliated with, endorsed by, or sponsored by Yelp. Yelp is a trademark of its respective owner. The tools work with publicly available data only, and you are responsible for using the results in line with Yelp's terms and the law that applies to you.
Compliance and personal data
The review tools return personal data. A review carries the author's display name, profile photo, stated location, user ID and a link to their profile, and reviewers are private individuals rather than businesses. That puts the response in scope of the GDPR and the CCPA in a way a business listing is not. Decide what you actually need before you store it, keep it no longer than the purpose requires, and check your own obligations. Aggregate analysis rarely needs the author fields at all.
HasData links
Yelp Scraper API, the REST endpoints behind these tools
Other HasData MCP servers: Google Search, Google Maps, Google Trends, Google Flights, DuckDuckGo, YouTube, TikTok, Instagram, Amazon, Zillow, Airbnb, Booking.com, Indeed.
Development
The launcher is a thin stdio bridge to the remote server, so there is nothing to build.
npm install
HASDATA_API_KEY=your_key_here npm testThe tests in test/ assert the tool contract, the part that can break without a commit here. They check that ?apis=yelp returns the expected tool count, that no name changed, that every tool still declares its required parameters and carries a description, and that the key in use is actually accepted. That last check calls a tool for real and costs 10 credits, which is the price of a canary that can fail for the right reason.
The contract suite also runs weekly on a schedule, because the upstream tool list can change without anyone touching this repository.
Contributing
A tool table, a response sample or a documented behaviour that does not match reality is worth an issue. There is a template for exactly that. Pull requests are welcome for the same, and for anything in the launcher.
License
MIT, see LICENSE.
Available Tools
3 toolshasdata_yelp_place_getPlaceDetailsyelp_place: GET /AInspect
Get Yelp Place Details
Fetches a single Yelp business by Yelp ID or alias with domain targeting. Returns name, address, phone, website, price range, categories, overall rating, review count, hours, amenities, photos, and highlighted reviews. Use to enrich leads or listings with verified Yelp metadata, monitor a competitor's rating and review count over time, or validate hours/amenities before displaying venue details to end users.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | No | Yelp domain to use. Default is `www.yelp.com`. | |
| placeId | Yes | The Yelp ID or Yelp Alias of the place. For example, 'jPIZ3FR5LNcwPuUHi2Fe4g' or 'mcdonalds-new-york-386'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It clearly indicates a read-only fetch and lists the returned fields, but it does not mention authentication needs, rate limits, or error behavior. This is adequate but not deeply transparent.
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?
The description is well-structured: a brief fetch statement, a concise enumeration of returned fields, and practical use cases. The opening line is slightly redundant with the title, but overall every sentence contributes useful context.
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 no output schema, the description compensates by listing expected return fields. Required and optional parameters are covered by the schema, and use cases give an agent enough context to invoke the tool appropriately. Minor gaps around errors and data freshness remain.
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% and both parameters are already documented with descriptions and an enum for domain. The description adds little semantic detail beyond what the schema provides, such as the notion of 'domain targeting' and the Yelp ID/alias examples.
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 states a specific action ('Fetches a single Yelp business by Yelp ID or alias') and identifies the resource and returned data. It clearly distinguishes itself from the sibling review and search tools by emphasizing 'single business' details.
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 provides concrete use cases: enriching leads, monitoring competitor ratings over time, and validating hours/amenities. It does not explicitly contrast with sibling tools, but the context makes it clear when this details endpoint is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hasdata_yelp_reviews_getPlaceReviewsyelp_reviews: GET /AInspect
Get Yelp Place Reviews
Fetches the review feed of a single Yelp business by Yelp ID, with sorting (relevance, date, rating, elites), filtering by star rating, language and free-text query, and cursor pagination. To page, either step start by num, or resend the pagination.nextPageToken of the previous response as the nextPageToken parameter, keeping the other filters unchanged, until pagination.hasNextPage is false. The response reports the page it returned as pagination.start and pagination.num, matching the parameter names. Each review returns the author profile (name, location, review/friend/photo counts, Elite status), full text with language, star rating, timestamp, attached photos and videos, reader reactions, review tags (check-ins, first review, reservation and payment flags) and the owner reply when present. The response also carries the business review totals broken down by rating and by language. Use to monitor customer sentiment over time, mine complaints for a competitor, track how a business responds to negative reviews, or build rating-distribution and review-velocity datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| num | No | Number of reviews to return per page. The maximum is 49. Defaults to 49 for the recommended feed and to 10 when `notRecommended` is set. The response echoes it back as `pagination.num`. | |
| query | No | Note: Yelp ignores the `rating` filter while searching, so a query returns matching reviews of every star rating. Free-text query to search within the reviews of the place. | |
| start | No | Result offset for pagination. It skips the given number of reviews, so the step matches `num` (e.g., 0, 49, 98 for the recommended feed, or 0, 10, 20 when `notRecommended` is set). The response echoes it back as `pagination.start`. Cannot be combined with `nextPageToken`. | |
| domain | No | Yelp domain to use. Default is `www.yelp.com`. | |
| rating | No | Note: Yelp ignores this filter when `query` is set, so a search returns matching reviews of every star rating. Filters the reviews by star rating. Possible values are 5, 4, 3, 2 and 1. To return only five-star reviews, set it to `5`. To include several ratings, pass them comma-separated, for example `5,4,3`. When omitted, reviews with any rating are returned. | |
| sortBy | No | The order in which the reviews are returned. Defaults to relevanceDesc. | |
| placeId | Yes | The Yelp ID of the place. For example, '-4ofMtrD7pSpZIX5pnDkig'. Yelp IDs can be obtained from the Yelp Search Scraper API. | |
| languageCode | No | Language of the reviews to return, as a two-letter code (e.g., 'en', 'es', 'fr'). Defaults to en. | |
| nextPageToken | No | Opaque cursor for the next page, taken verbatim from `pagination.nextPageToken` of the previous response. It carries both the offset and the page size, so passing it alone continues the feed where the last response ended. Keep the other filters (`sortBy`, `rating`, `languageCode`, `query`, `notRecommended`) identical across pages. Use either this or `start`, not both. Paginate until `pagination.hasNextPage` is false. | |
| notRecommended | No | Returns the reviews Yelp does not currently recommend (filtered out of the main feed by its recommendation software) instead of the recommended ones. These reviews carry no photos, videos or reactions, and are paginated ten at a time. Defaults to false. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It thoroughly describes pagination mechanics, nextPageToken semantics, the conflict between query and rating filters, notRecommended limitations, response echo fields, and the contents of each review and the business totals. This is exceptionally transparent for an unannotated tool.
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?
The description is long but justifiably so for a 10-parameter tool with subtle pagination and filter interactions. It is front-loaded with a clear summary, then logically organized into pagination, response contents, and use cases. Every sentence contributes useful information without fluff.
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?
There is no output schema or annotations, so the description must explain both invocation and expected returns. It covers pagination fields, review contents, author profile fields, photos/videos/reactions, tags, owner replies, and rating/language totals. This is a complete and self-sufficient description for selecting and invoking the tool 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 coverage is 100%, so the baseline is 3. The description adds meaningful cross-parameter guidance beyond the schema: step start by num, resend nextPageToken while keeping filters unchanged, do not combine start with nextPageToken, and paginate until hasNextPage is false. It also clarifies default behavior for num with and without notRecommended, which adds value beyond the individual parameter descriptions.
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 the specific resource 'Yelp Place Reviews' and a clear verb, then states it fetches the review feed of a single Yelp business by Yelp ID with sorting, filtering, and pagination. This clearly distinguishes it from siblings like place details and search results, even without naming them.
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 gives concrete use cases: monitoring sentiment, mining complaints, tracking owner responses, and building rating-distribution or review-velocity datasets. It also explains when filters behave unexpectedly, such as Yelp ignoring the rating filter when a query is set. However, it does not explicitly state when not to use this tool compared to the sibling search or details tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hasdata_yelp_search_getSearchResultsyelp_search: GET /AInspect
Get Yelp Search Results
Runs a Yelp business search by keyword and location with optional map-bounded radius via the l parameter (g:lon1,lat1,lon2,lat2), domain targeting, and offset-based pagination. Returns a ranked list of businesses with Yelp alias/ID, name, categories, rating, review count, price tier, neighborhood, and thumbnail. Use the returned aliases as input to the Yelp Place endpoint for full details, to power local-discovery UIs, or to build market-share/competitor datasets for a niche in a given geography.
| Name | Required | Description | Default |
|---|---|---|---|
| l | No | Parameter defines the distance or map radius for the search results. For example: `g:-95.2486,29.8496,-95.4277,29.6324`. | |
| start | No | Result offset for pagination (e.g., 0 for the first page, 10 for the 2nd page, etc.). | |
| domain | No | Yelp domain to use. Default is `www.yelp.com`. | |
| keyword | Yes | The search term for which to get the search results. | |
| location | Yes | The location where to search for businesses with the given keyword. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden and covers core behavior: keyword/location search, optional map-bounded radius, domain targeting, offset-based pagination, and a ranked list of businesses with specific fields. It does not mention rate limits or response envelope details, but the disclosed behavior is solid for a GET search tool.
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?
The description is concise, front-loaded with the operation, and uses every sentence for meaningful detail. The opening line is slightly redundant with the title, but the rest of the description is tightly written and well organized.
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?
Since there is no output schema, the description usefully enumerates returned fields and downstream usage. It also explains pagination and domain targeting. It is missing notes on authentication, rate limits, or explicit sibling routing, but it gives enough context to invoke the tool correctly and interpret results.
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 the baseline is 3. The description adds minor framing around `l` as map-bounded radius and `start` as pagination offset, but the schema already documents these parameters. It does not significantly deepen parameter understanding beyond structured definitions.
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 identifies a specific operation: Yelp business search by keyword and location. It clearly distinguishes itself from sibling tools (place details, reviews) and lists concrete result contents. The purpose is immediately understandable.
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 explicitly says to use search results and then pass returned aliases to the Yelp Place endpoint for full details, and gives use cases like local-discovery UIs and market-share datasets. It does not explicitly mention when to use Reviews or state exclusions, but the intended placement in the workflow is clear.
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.
3 tool updates
v1.0.0- First observed
hasdata_yelp_place_getPlaceDetails - First observed
hasdata_yelp_reviews_getPlaceReviews - First observed
hasdata_yelp_search_getSearchResults
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: search finds businesses, place details returns metadata for a single business, and reviews returns the review feed. There is no functional overlap between them.
All tools follow the same hasdata_yelp_<area>_get<Thing> pattern, using a consistent snake_case prefix with CamelCase action names. The naming is predictable and makes the resource type obvious.
Three tools is a well-scoped set for a Yelp data access server. Each tool covers a core need—search, business details, and reviews—without redundancy.
The toolset forms a complete read-only workflow: search for businesses, fetch place details, and retrieve reviews. There are no dead ends, and write operations are outside the apparent scope of the server.
Maintenance
Related MCP Connectors
4 Yelp endpoints. Pay per call in USDC via x402.
Yandex search results, images, and SERP data via the Apify Yandex Search Scraper, hosted MCP.
Live Google Maps business search, review, and photo data for AI agents over MCP.
Google Shopping products, prices, sellers, and deals as structured data via a hosted MCP server.
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