TGGET Niche Research MCP server

• compare_researchwrite actionFree

  Puts two to five finished niche scans side by side to help choose between ideas. `items` are the niches with their numbers, the report's positioning and napkin economics, and `wins`: in how many rows the niche holds the better value. `rows` are the ranked measures (verdict, demand, trend, cost per click, weak search results, competitor sites, App Store apps, fresh community signals) with the value of every niche and `best`, the indexes of the items that hold the better value; `better` says which way is better (max or min), null when the row is shown but not ranked. Demand and cost per click are not ranked across different markets (`mixed_markets`). The marks are computed from numbers, not written by a language model. Every scan needs a finished report. `share_url` is a read-only link to this comparison that works without signing in: give it to the person only when they want to share the comparison, it shows the numbers and the conclusions of their researches to whoever holds it.

• follow_nicheFree

  Starts following the niche of a finished scan that has a report: the scan becomes the first measurement and the niche is scanned anew every month, with a digest of what changed sent to the account. Monthly checks do not spend researches; the plan limits how many niches are followed (FREE 1, Starter 2, Pro 10). Following a niche that is already followed returns its project.

• get_projectFree

  Returns a followed niche with the history of its checks, newest first: the first measurement (`baseline`) and the monthly checks, each with its numbers, what changed since the previous check and the id of the research it came from. Pass that `research_id` to get_report to read the fresh report of a check. Changes are computed from numbers and lists, not written by a language model.

• get_reportFree

  Returns the final niche report of a finished scan (or the report research itself) as Markdown: verdict, what product to build (customer pains with evidence and severity, positioning, MVP with the pain each feature relieves, platform, monetization, competitor matrix), napkin economics (price hypothesis, acquisition cost from the cost per click in paid search, revenue per customer, whether it adds up, assumptions), SEO plan for the site (clusters, pages with title/H1/description, content plan, FAQ), ASO for App Store and Google Play, name options with domain and App Store availability, landing page draft, distribution channels by type with a first step, effort and priority, validation steps and risks. Pass format "json" for structured data (keys: verdict, product, economics, seo, aso, names, landing, channels, validation, risks).

• get_researchFree

  Returns the current state and findings of a research by id: status, headline numbers, ranked candidate phrases, competitor overview, child researches (sub-topics, competitor checks, signals) with their ids, and skipped stages. Call repeatedly until status is completed and pending is 0.

• list_projectsFree

  Lists the niches the account follows. A followed niche (a project) is scanned anew once a month and compared with its previous check. For each project: status, whether the plan serves it, the date of the next check, the latest numbers (verdict, demand, cost per click, competitor sites, App Store, community signals) and what changed at the latest monthly check, as structured `changes` and as `digest` lines of text. Also returns how many niches the plan follows.

• list_researchFree

  Lists the researches the account started itself, newest first: niche scans of a phrase and descriptions of an idea, without the checks they spawned. Use it to find the id of an earlier research before get_research, get_report, compare_research or follow_niche, or to see what is still running. Each item has `id`, `kind`, `phrase`, `status` (queued, running, completed, failed), `mode`, `created_at`, `url` of its page and `total_count`: monthly searches of the phrase, null for a description. `quota` tells how many researches the plan has left. Reads only; spends nothing.

• run_signalwrite actionFree

  Adds one check to a finished research and returns the id of the new child research. Use it when the scan skipped a stage, when a person asks about one phrase in particular, or to rebuild the report after new checks. A niche scan already runs the usual checks by itself, so this tool is for what is missing. Checks added this way are free: they do not spend researches of the plan. Checks of a demand research (`kind`): - `competitors`: who holds the search results for a phrase of the research; pass the phrase in `phrase`. - `dynamics`: two years of demand history. - `appstore`: App Store apps for the phrase with ratings and freshness. - `wiki`, `radar`, `youtube`: public interest and community signals. - `paid`: paid search for the phrase and its strongest candidates: volume, advertiser competition, cost per click. - `insight`: a summary of the findings written by the language model. - `report`: the final niche report, written anew from the current data; needs a finished niche scan. Checks of a child research: - `fingerprint`: product sites found by a competitors check, with pricing and app links; the parent is that competitors check. - `reviews`: recent App Store reviews of the leaders; the parent is an App Store research. - `insight` also accepts a competitors check as the parent. The parent must be completed and belong to the account. The check runs in the background: poll get_research with the returned `id` every 20-30 seconds until `status` is `completed`. `insight` and `report` are rebuilt at most once an hour.

• start_researchFree

  Starts a niche research from a short search phrase or a plain-language description of an app or service idea. A phrase is scanned directly (demand, sub-topics, competitors, signals); a description is first turned into real search queries, the best one is then scanned automatically. Returns the research id to poll with get_research. Counts as one research of the plan allowance, whether the data comes from the sources or from the shared cache.

• update_projectwrite actionFree

  Pauses a followed niche (`paused`: no monthly checks, its place in the plan is freed), follows it again (`active`, needs a free place in the plan) or stops following it (`deleted`: the project with its checks and digests is removed for good, the researches stay in the history). Ask the person before deleting.

• –Code scanremote-only server, no package to scann/a
• Live reliabilityremote reachable in 785ms20/20
• Tool poisoning10 tool descriptions checked15/15
• Auth qualityAPI key sent as a header8/15
• Maintenancelast push 2 days ago15/15
• Maintainer identityregistry namespace matches repository owner; website matches verified namespace8/10

Safety score

88/100

higher than 85% of 2,174 E-commerce servers

Tools

10

the typical E-commerce server has 6

Tools that change things

30%

typical: 0%. Fewer write tools means less an agent can break

Uptime

96%

of our recent checks; the typical remote E-commerce server answers 100%

Response time

660 ms

faster than 22% of 1,527 E-commerce servers

TGGET Niche Research: common questions

Is TGGET Niche Research MCP server safe?

Yes, by our scan: it is graded A (88/100). Read the TGGET Niche Research safety report

How do I install TGGET Niche Research?

It runs remotely at tgget.io. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.

Does TGGET Niche Research need an API key?

Yes. The registry entry asks for Authorization.

Is TGGET Niche Research maintained?

The last commit was 36 days ago (2026-09-29). The latest release is v1.0.1.

Is TGGET Niche Research up?

96% of our last 28 checks got an answer. We check remote servers about four times a day.

What can I use instead of TGGET Niche Research?

Servers from other publishers that do the same job: AsoBeast MCP server, Apify MCP server and Idea Reality MCP server.

How does TGGET Niche Research compare with other E-commerce MCP servers?

safety score 88/100 (higher than 85% of 2,174 E-commerce servers); tools 10 (the typical E-commerce server has 6); tools that change things 30% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.