# FrontStat

**Canonical:** https://apis.io/providers/frontstat-com/  
**Website:** https://frontstat.com/  
**APIs profiled:** 0

FrontStat provides an AI visibility tracking service for startups, allowing them to see within seconds whether large language models like ChatGPT, Claude, and Gemini recommend their product. The platform aggregates model recommendations, highlights naming discrepancies, and offers actionable fixes to improve AI-driven discoverability.

## Kin Score — 19.3 / 100 (emerging)

Scored 2026-11-13 under rubric 0.28.0.

| Facet | Score |
|---|---|
| Discoverability | 48.3 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 27.9 |
| Access Clarity | 53.2 |

Regulatory layer — **Horizontal (data, software, accessibility, platform)**: 3.6 (matched via fallback).

## Agent readiness — 6.7 (agent-aware)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | documented |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | unknown |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |
| MCP Tool Quality | na |

## Access

Freemium — onboarding: unknown, pricing: freemium, trial: no (confidence: low).

## MCP servers (1)

- **FrontStat MCP Server** — Remote MCP server at frontstat.com.

## Security (1)

- **FrontStat Domain Security** — TLSv1.3 · HSTS · DMARC

## Plans (1)

- **FrontStat Plans Pricing**

## Tags

Artificial Intelligence, Visibility, Startups, Analytics, Platform, Company

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/frontstat-com/). Scores are computed from the provider's own public artifacts under a published rubric.