# Local Experience Post-Visit Plan AI Agent Connect > Local Experience Post-Visit Plan MCP handles the messy details after a user finishes an activity. It reconciles what people liked with what providers allow, managing everything from privacy settings and thank-you notes to suggesting what they should do next. Your AI client uses this to close the loop on every experience automatically. ## Overview - **Category:** personalization - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_bIEBwh5h2NA5dtCcdp4b2iYNLIioo26tE2Yut0Si/ai-agent-connect - **Tags:** feedback, engagement, privacy, user-experience, automation ## Description You shouldn't have to manually sort through feedback or guess if a user is allowed to return to a venue. This MCP acts as an intelligence layer for post-experience workflows. It looks at what happened during a visit and compares it against provider rules and user preferences. When a user finishes a tour, a workshop, or an adventure, your agent can immediately determine if the data is safe to share or if it needs to stay private. It handles the social side too, by drafting the right responses for providers or creating summaries of the best moments. Instead of just ending a session, you use this to build a bridge to the next one. It checks if return visits are even an option and then suggests a new activity that actually fits the user's mood. It's about turning a single event into a continuous, managed relationship. ## Tools ### analyze_privacy_and_sharing This tool checks if experience data can be shared publicly. It looks at user consent and provider terms to decide what stays private. ### synthesize_user_records This tool pulls out the specific parts of an experience a user wants to keep. It helps preserve important memories in their history. ### construct_next_experience_prompt This tool builds a personalized suggestion for a future activity. It uses previous impressions to guide the next move. ### evaluate_return_viability This tool checks if a user can or should return to a provider. It weighs the outcome of the last visit against specific provider policies. ### generate_engagement_actions This tool drafts the right follow-up messages. It creates thank-you notes or feedback reports to send to the provider. ## Prompt Examples **Prompt:** ``` I had a great time at the pottery workshop, but I'm not sure if I can share the photos I took. ``` **Response:** ``` Based on your feedback, your data can be kept private. Since you enjoyed the workshop, I can also help you draft a thank-you note to the instructor. ``` **Prompt:** ``` The guided tour was okay, but the guide was late and it was quite frustrating. ``` **Response:** ``` I have flagged this as a high priority issue. I will draft a formal feedback report regarding the guide's tardiness to help resolve this with the provider. ``` **Prompt:** ``` I loved the mountain biking trip! I want to do more outdoor adventures next month. ``` **Response:** ``` Since you enjoyed the mountain biking, how about exploring some local trail running routes or a forest hiking excursion next month? ``` ## Capabilities ### Privacy Management Your agent uses this to verify if data sharing complies with user consent. ### Automated Follow-ups The AI drafts thank-you notes or formal feedback reports based on the visit outcome. ### Future Planning Your client suggests new activities based on what the user just did. ### Policy Enforcement The agent checks provider terms to see if return visits are permitted. ### Memory Preservation The tool identifies and saves specific experience highlights for the user's records. ## Use Cases ### Post-Tour Feedback After a guided tour, the agent drafts a report if the user had a bad experience. ### Workshop Follow-up The agent sends a thank-you note to a pottery instructor after a successful class. ### Adventure Planning A user finishes mountain biking and the agent suggests trail running for next month. ### Privacy Compliance The agent checks if a user's photos from a local event can be shared online. ## Benefits - Automates the drafting of thank-you notes and feedback reports. - Ensures data sharing stays within user consent and provider rules. - Reduces manual work by suggesting the next logical activity for users. - Maintains organized user histories by extracting key experience moments. ## How It Works Connect your client to Vinkius and start running post-visit workflows immediately. 1. Connect your MCP-compatible client to Vinkius. 2. Provide the user's feedback or experience details to your agent. 3. The agent uses the MCP tools to analyze privacy, policy, and sentiment. 4. The agent generates the requested actions like notes, reports, or suggestions. ## Frequently Asked Questions **What does this MCP actually do?** It manages the workflow that happens after a user completes an experience. It handles privacy, feedback, and planning for future activities. **Which AI clients can I use with this?** You can use this with any MCP-compatible client, including Claude, Cursor, Windsurf, and VS Code. **How does it handle privacy?** It uses the analyze_privacy_and_sharing tool to check user consent and provider terms before any data is shared. **Can it help me suggest new things for users to do?** Yes. It uses the construct_next_experience_prompt tool to suggest activities based on what the user just experienced. **Do I need to host this myself?** No. Vinkius hosts and manages the MCP for you. You just connect your client and start using the tools. **How does the server handle user privacy?** The `analyze_privacy_and_sharing` tool ensures that data is only shared if explicit user consent is provided and no provider terms prohibit it. **Can I automate thank-you notes to providers?** Yes, `generate_engagement_actions` creates appropriate responses, such as thank-you notes for positive experiences or issue reports for unresolved problems. **How are future activity suggestions generated?** The `construct_next_experience_prompt` tool uses both the user's future interests and their previous impressions to create personalized suggestions.