Group Activity Comparator

Group Activity Comparator MCP Connector for Claude

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Rank and compare group activities based on cost, travel, and accessibility.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a decision-support engine for planning group outings. It allows AI agents to find available activities using find_activities, generate prioritized lists via calculate_rankings, perform head-to-head comparisons with compare_activity_pairs, and evaluate specific needs through get_accessibility_score. The engine evaluates activities against logistical constraints like group size, budget, and travel effort, applying user-defined weights to find the best fit for any group.

group-planningrankinglogisticsaccessibilityoptimization

4 tools expose this connector's capabilities to your AI agent.

calculate_rankings

0). Optional constraints like maxBudget or maxDuration can be provided. Generates a ranked list of activities based on user-defined weights and group requirements

compare_activity_pairs

Provides a head-to-head comparison between two specific activities

find_activities

Retrieves all available activities that meet the baseline availability criteria

get_accessibility_score

g., ["wheelchair_access", "low_noise"]). Evaluates how well an activity accommodates specific group needs regarding accessibility

See how to talk to your AI agent using Group Activity Comparator.

Find activities available on 2025-06-15 in London.

I found three activities available in London on June 15th, 2025: The British Museum, Hyde Park stroll, and the London Eye.

Rank these activities for a group of 5 people, prioritizing low cost and low travel effort.

Based on your preferences, the top-ranked activity is the local park, followed by the community center.

Compare the Museum and the Zoo for a group that needs wheelchair access.

The Museum is the better choice as it provides full wheelchair access, whereas the Zoo only meets partial requirements.

Activities are ranked by normalizing metrics like cost and travel effort, then applying the weights you provide to `calculate_rankings`.

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