About vischeck
vischeck is a local bridge for founders and product teams checking how AI search engines present a startup product. It forwards FrontStat’s launch lookup and Pro report tools without storing visibility data itself.
Key features
- 01Public launch visibility lookup
- 02Pro visibility reports
- 03Competitor and cited-site reports
- 0430-day visibility history
- 05CLI and stdio MCP modes
Use cases
- 01Check whether AI engines recommend a tracked launch
- 02Review competing products named in Pro reports
- 03Find sites cited for buyer questions
- 04Track daily visibility changes
MCP support
It exposes FrontStat’s upstream tool list through a local MCP server and forwards tool calls and results. The public lookup needs no token; five Pro tools require a FrontStat Pro dashboard token.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
6Saved tool list · updated 1w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| domain | string | Yes | The product's domain or URL, for example example.com. |
Details
From the project READMEvischeck
vischeck is a small local bridge to FrontStat's hosted MCP server, which tracks whether the AI search engines recommend a startup's product. Ask about a launch, and the answer shows a visibility score and whether ChatGPT, Claude and Gemini name the product for its buyer question — or which competing products they name instead.
The bridge keeps no visibility data of its own. It serves the upstream server's tool list locally, exactly as captured from the live endpoint, and forwards each tool call upstream, returning the result untouched. The whole captured surface only reads: a public launch lookup that needs no token, plus five Pro report views that need a FrontStat Pro dashboard token.
Tools
check_public_launch— look up a product launch that FrontStat already tracks: its name, its rank on this week's or this month's launch board, its visibility score (0 to 100), and whether ChatGPT, Claude and Gemini recommend it for its buyer question, with the products they name instead, plus a link to its public FrontStat page. If the domain is not tracked yet, it says so and links to the free report. Required:domain(the product's domain or URL, e.g.example.com). No token needed.get_visibility— your latest FrontStat Pro report: date, visibility score, whether each AI search engine recommends your product for your main buyer question and who it names instead, and per buyer question how many answers recommend you (X of Y) per assistant. Needs a Pro token. Takes no arguments.list_buyer_questions— each buyer question FrontStat asks the AI search engines about your market, with a yes or no per engine for whether the answer recommends your product. Needs a Pro token. Takes no arguments.who_wins_instead— the products the AI search engines name instead of yours, and the sites they cite, each with how many answers it appears in, from your latest Pro report. Needs a Pro token. Takes no arguments.cited_sites— the sites the AI search engines cite most when they answer your buyer questions, with how many answers cite each one, your own domain left out: the pages to get mentioned on. Needs a Pro token. Takes no arguments.visibility_history— one point per day for the last 30 days: your visibility score and how many answers recommend your product, so you can see whether your changes moved the AI search engines. Needs a Pro token. Takes no arguments.
Configuration
FRONTSTAT_MCP_URL(optional) — override the upstream endpoint. Defaults tohttps://frontstat.com/api/mcp. (The override hook is this rebuild's own labelled reconstruction.)FRONTSTAT_API_KEY(optional) — when set, every upstream request carries anAuthorization: Bearer <value>header, which is how the five Pro tools receive the dashboard token. When it is not set, no Authorization header is sent and only the public launch lookup works. (This forwarding is also a labelled reconstruction; the original surface was captured keyless.)
Forwarding mechanics
On the first tool call the client opens a streamable
HTTP session with the upstream: a JSON-RPC initialize
request (protocolVersion 2025-03-26, clientInfo name
vischeck), then a notifications/initialized
notification, then the tools/call request carrying
the caller's arguments unchanged. When the upstream
answers initialize with an Mcp-Session-Id header,
that value rides on every later request of the session,
which is reused for the life of the process. Replies
may arrive as plain JSON or as server-sent events;
both are read.
Successful upstream results pass through verbatim,
including any isError flag the upstream sets.
Failures — a JSON-RPC error, a non-2xx status, an
unreadable response, or a request that cannot be
completed — come back as an isError result whose
text starts with vischeck error: (this rendering
is this rebuild's own labelled reconstruction).
CLI
Each tool is also a subcommand taking one JSON argument string (omit it for tools that take none):
npx @reneturcios/vischeck check_public_launch '{"domain":"example.com"}'
npx @reneturcios/vischeck get_visibility
npx @reneturcios/vischeck who_wins_instead '{}'
npx @reneturcios/vischeck visibility_history
npx @reneturcios/vischeck --version
Exit codes: 0 on success, 1 on an isError
result, 2 on a usage error.
MCP server
npx -y @reneturcios/vischeck mcp
MCP client config:
{
"mcpServers": {
"vischeck": {
"command": "npx",
"args": ["-y", "@reneturcios/vischeck", "mcp"]
}
}
}
Docker
docker run -i ghcr.io/cloudcorprecords/vischeck
docker run --rm ghcr.io/cloudcorprecords/vischeck --version
The container's default command is mcp, so the first
form is a stdio MCP server; any other arguments reach
the CLI normally.
Development
npm install
npm run build
npm test
npm run dev
The test suite runs entirely against a stubbed JSON-RPC upstream — no live network calls.
License
MIT — see LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.