mcp server
Ninar AI
Audit your brand's visibility across ChatGPT, Gemini, Claude, Perplexity + 6 more engines.
Description as published by the maintainer. Source
- version 1.0.0
- archived
archived — The linked repository returns 404. It was deleted, renamed or made private.
What this server can do
5 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
audit_brand_visibility(entity, taxonomy_id, raw_evidence)- Check whether a brand or entity surfaced by an AI engine is a genuine competitor in your category (e.g. is 'Banner Life' actually a mortgage insurance competitor to Enact?). Uses dual-model verification with automatic escalation on disagreement. Returns a confirmed/rejected decision, confidence score, reasoning, and audit trail. Pro plan or higher required. Required: entity, raw_evidence.
generate_content(gap_type)- Generate AI-optimized content (FAQ, about copy, use cases, differentiators) for the gaps in your latest scan. Returns full content text inline — no need to visit the dashboard. Pro plan or higher required. Pass gap_type='all' to get every block in one call. Required: gap_type.
get_latest_score- Get the AI Visibility Index (0-100) for the signed-in user's most recently scanned brand, broken down by engine. Requires a free Ninar account (no credit card).
list_content_gaps- List AI-generated content suggestions (FAQs, differentiators, use cases, about copy) the signed-in user can publish to close visibility gaps found in their latest scan.
scan_visibility(city, country, website, category, use_case, brand_name)- Run an AI visibility scan for a brand. Pass `city` for a local-business check (ChatGPT + Gemini, city-scoped). Omit `city` for a multi-engine GEO scan across ChatGPT, Gemini, Perplexity, Claude, AI Overviews — engine count scales with the user's Ninar plan (free = 2). Required: brand_name, category.
Last successful function declaration observed on . Source: https://ninar.ai/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://ninar.ai/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-05-21 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-05-21 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | not_found | GitHub returned 404 for the repository the maintainer listed. The project was deleted, renamed or made private, so the listing points at nothing. | as of fetch | GitHub | |
| mcp tools declared | 5 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | ninar.ai | |
| mcp endpoint status | ok | The server listed 5 functions when asked. | as of probe | ninar.ai |
Where to get it
This record as data
Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.
GET /api/v1/entries/mcp_server.json