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MCP Gateway for Analytics with Granular Permissions
Build a middleware MCP server that sits between any analytics platform and AI agents, providing fine-grained permission control over what data agents can read or configure. The key differentiator is a permission tier that exposes only aggregated, non-PII data to agents, solving the security concern of piping raw visitor data through third-party models.
Pourquoi c'est important
You run a development team that has enthusiastically adopted AI agents for routine work — writing queries, summarizing reports, checking metrics. But when it comes to analytics, you hit a wall. Your agents have no way to read your traffic data, and the few MCP integrations that exist hand agents full write access, which your security team will never approve. Even if you grant read-only access, you worry that raw visitor IPs and session IDs flow through to a model provider you do not fully control. You want your agents to answer questions like 'what drove last week's traffic spike?' without exposing PII or risking accidental configuration changes. Today you manually export spreadsheets and paste them into chat windows, which defeats the purpose of automation.
- · Conçu pour Development teams and technical marketers who use AI agents (Claude, ChatGPT, Cursor) for routine analytics work but need security review approval before connecting agents to production data.
- · Monétisation la plus probable : SaaS subscription.
La douleur · Récit
You run a development team that has enthusiastically adopted AI agents for routine work — writing queries, summarizing reports, checking metrics. But when it comes to analytics, you hit a wall. Your agents have no way to read your traffic data, and the few MCP integrations that exist hand agents full write access, which your security team will never approve. Even if you grant read-only access, you worry that raw visitor IPs and session IDs flow through to a model provider you do not fully control. You want your agents to answer questions like 'what drove last week's traffic spike?' without exposing PII or risking accidental configuration changes. Today you manually export spreadsheets and paste them into chat windows, which defeats the purpose of automation.
Détail du score
Signal du marché
Mise sur le marché
Technical product managers and engineering leads at startups and mid-size companies who already use Cursor or Claude for development and want to extend agent usage to analytics workflows
~30-50K development teams globally actively using MCP-compatible agents with interest in analytics integration
Developer community launch on Hacker News and Product Hunt, supported by technical blog content about MCP permission patterns
$29/month for team plan with up to 5 agent connections
25 paying teams within 30 days of launch, with at least 10 providing feedback on permission tier usefulness
Périmètre MVP · 1–2 semaines
- Define MCP server schema with read and configure permission scopes mapped to specific data categories
- Build core MCP server in TypeScript that exposes aggregated analytics endpoints (pageviews, top sources, goal completions)
- Implement OAuth flow with scoped tokens distinguishing read-only vs configure access
- Create aggregated-only data layer that strips IPs, hashed IDs, and session-level identifiers before responding to agent queries
- Write basic audit logger recording every agent request, permission level, and response summary
- Build adapter for at least one analytics backend (start with a simple internal tracker or Plausible API)
- Create 5 pre-built agent prompt templates for common analytics questions (traffic summary, top pages, source breakdown, goal funnel, weekly comparison)
- Set up EU-hosted infrastructure (Netherlands region) with data residency documentation
- Build minimal dashboard showing connected agents, their permission levels, and audit log entries
- Write integration tests verifying that read-only scoped requests never return raw visitor-level data
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1Major analytics vendors like Plausible or Fathom could ship native MCP support with permission tiers, eliminating the need for a standalone middleware layer
- 2The MCP protocol is still early and evolving; a breaking change could require significant rework with no guarantee of backward compatibility
- 3Teams may decide the security risk of any agent-to-analytics connection is unacceptable regardless of permission controls, preferring manual export workflows
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
Approximately 3 commenters expressed strong interest in AI agent integration with analytics, with one specifically highlighting that the read vs configure OAuth split would be the detail that gets internal approval. One commenter raised a critical concern about whether read-only access still exposes raw visitor-level data to models they do not control, revealing that permission granularity alone is insufficient without a data aggregation layer. The founder's own narrative confirms agents had no way into analytics before this MCP integration, validating the gap.
Plan d'Action
Validez cette opportunité avant d'écrire du code
Prochaine Étape Recommandée
Construire
Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.
Kit de Textes pour Landing Page
Textes prêts à coller, basés sur le langage réel de la communauté Reddit
Titre Principal
MCP Gateway for Analytics with Granular Permissions
Sous-titre
Build a middleware MCP server that sits between any analytics platform and AI agents, providing fine-grained permission control over what data agents can read or configure. The key differentiator is a permission tier that exposes only aggregated, non-PII data to agents, solving the security concern of piping raw visitor data through third-party models.
Pour Qui
Pour Development teams and technical marketers who use AI agents (Claude, ChatGPT, Cursor) for routine analytics work but need security review approval before connecting agents to production data
Liste des Fonctionnalités
✓ MCP server with OAuth-scoped read vs configure permissions ✓ Aggregated-only data tier that blocks raw IPs, session IDs, and hashed identifiers from agent queries ✓ Audit log of all agent queries and actions for compliance review ✓ Support for multiple analytics backends (own platform plus GA4, Plausible, etc.) ✓ Pre-built prompt templates for common analytics questions agents can answer ✓ Universal MCP proxy with configurable data redaction rules per SaaS connector ✓ PII detection and automatic aggregation before responses reach agent models ✓ Permission tiers: read-aggregated, read-raw, configure, with admin approval workflows
Où Valider
Partagez votre landing page sur r/Product Hunt · analytics — c'est exactement là que ces points de douleur ont été découverts.
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