What is the main functional difference between TGGET Niche Research and Infranodus MCP Server Infranodus?

TGGET Niche Research is categorized under Research and uses an install path we have not confirmed yet. Infranodus MCP Server Infranodus belongs to Research and uses a local stdio subprocess. Pick TGGET Niche Research for research work and Infranodus MCP Server Infranodus for research.

How do I install TGGET Niche Research or Infranodus MCP Server Infranodus in Claude Desktop, Cursor, or Windsurf?

Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. TGGET Niche Research has no confirmed install command yet — follow its own setup instructions.

Are TGGET Niche Research and Infranodus MCP Server Infranodus free to use, or do they require API keys?

TGGET Niche Research has no declared pricing — check the project's own site. Auth: api key required. Infranodus MCP Server Infranodus has no declared pricing — check the project's own site. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.

Can I run both TGGET Niche Research and Infranodus MCP Server Infranodus at the same time in my AI client?

Yes. MCP clients support multiple servers: add tgget-niche-research and infranodus-mcp-server-infranodus as separate keys in the same mcpServers object, and your assistant will call tools from either one.

Which MCP server has higher directory engagement and quality ratings?

On AllMCPs, TGGET Niche Research has a quality signal of 53/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Infranodus MCP Server Infranodus has 65/100 (Great) with 4 views, 0 installs and 102 GitHub stars.

TGGET Niche Research vs Infranodus MCP Server…

TGGET Niche Research vs Infranodus MCP Server… | AllMCPs

Feature & specification comparison

Rows where the two servers differ are marked.

Spec	T TGGET Niche Research	Infranodus MCP Server Infranodus
Category	Research	Research
Quality signal	53/100 · Good	65/100 · Great
Install command	Not confirmed yet	Confirmed
Transport	—	Local subprocess (stdio)
Auth	API key required	API key required
Pricing	Not declared	Not declared
Required env vars	`TGGET_TOKEN`	None listed
Verified owner	Community listing	Community listing
GitHub stars	0	102
Directory activity	0 views 0 installs 0 upvotes	4 views 0 installs 0 upvotes
Tools	10	40
Full listing	View TGGET Niche Research	View Infranodus MCP Server Infranodus

Tools & capabilities

TGGET Niche Research tools 10

start_researchStarts 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. Demand is measured in one country and one language: `country` and `language` when given, otherwise the country and the language of the phrase (English: United States, Spanish: Spain, Portuguese: Brazil); write the phrase in the language people search in there. `country` and `language` of the answer tell how the numbers were measured. 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.
get_researchReturns the current state and findings of a research by id: status, `country` and `language` the demand was measured in (null where a source does not measure by country), 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.
get_reportReturns 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).
list_researchLists 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`, `country` and `language` the demand was measured in, `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_signalAdds 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.
compare_researchPuts 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. Every item names the `country` and the `language` its demand was measured in; demand and cost per click are not ranked across different markets, countries or languages (`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.
list_projectsLists 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.
get_projectReturns 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.
follow_nicheStarts 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. The niche is followed in the country and the language its scan was measured in (`country` and `language` of the project), so every monthly check measures the same audience.
update_projectPauses 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.

Infranodus MCP Server Infranodus tools 40

generate_knowledge_graphGenerate a knowledge graph with main topics, topical clusters, concepts, concepts (nodes) relations (edges) and structural gaps. Only use when explicitly asked to analyze a text or generate a knowledge graph. Do not use for short clarifying questions that you already have an answer to from the context of the conversation.
create_knowledge_graphCreate a knowledge graph in InfraNodus from text or from a URL, save it, and provide its name and a link to it for future use.
generate_ontology_graphUse AI to generate a reasoning ontology knowledge graph (entities and the relations between them) and optionally save it as a InfraNodus graph. Three sources, provide exactly one: prompt (a topic — one AI call), text (a long document or a structural digest of a project, chunked server-side), or sourceGraphName (an existing graph — e.g. a fully ingested repo, vault, or corpus — whose statements are read back, chunked, and condensed into an ontology). Set ontologyMode: 'codebase' for software projects, or 'procedural' to write a DIGEST of how the project works (prose statements with [[wikilinks]], not relation triples) from an already-uploaded graph — save it as <repo|vault>-<project>-digest for optimize_knowledge_base. Use to get a rich overview, a reasoning map of a topic, or a condensed 'how it fits together' graph of a large corpus.
memory_add_relationsAdd relations to the InfraNodus memory from text, save it, and provide its name and a link to it for future use.
memory_get_relationsProvide a list of relations from the InfraNodus memory for a given concept or entity
analyze_existing_graph_by_nameExtract and analyze the content of an existing InfraNodus graph from your account.
analyze_textExtract and analyze a graph from text, URL, YouTube video transcript, or an existing InfraNodus graph.
generate_content_gapsGenerate content gaps from text, URL, or an existing graph using knowledge graph analysis.
generate_topical_clustersGenerate topics and clusters of keywords from text, URL, or an existing graph using knowledge graph analysis.

Ready-to-paste client configs

Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.