mixpanel
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_today_top_eventsB | Get today's top events from Mixpanel. Useful for quickly identifying the most active events happening today, spotting trends, and monitoring real-time user activity. |
| profile_event_activityB | Get data for a profile's event activity. Useful for understanding individual user journeys, troubleshooting user-specific issues, and analyzing behavior patterns of specific users. |
| get_top_eventsB | Get a list of the most common events over the last 31 days. Useful for identifying key user actions, prioritizing feature development, and understanding overall platform usage patterns. |
| aggregate_event_countsB | Get unique, general, or average data for a set of events over N days, weeks, or months. Useful for trend analysis, comparing event performance over time, and creating time-series visualizations. |
| aggregated_event_property_valuesB | Get unique, general, or average data for a single event and property over days, weeks, or months. Useful for analyzing how specific properties affect event performance, segmenting users, and identifying valuable user attributes. |
| query_insights_reportB | Get data from your Insights reports. Useful for accessing saved analyses, sharing standardized metrics across teams, and retrieving complex pre-configured visualizations. |
| query_funnel_reportA | Get data for a funnel based on a funnel_id. Useful for analyzing user conversion paths, identifying drop-off points in user journeys, and optimizing multi-step processes. Funnel IDs should be retrieved using the list_saved_funnels tool. |
| list_saved_funnelsA | Get the names and IDs of your saved funnels. Useful for discovering available funnels for analysis and retrieving funnel IDs needed for the query_funnel_report tool. |
| list_saved_cohortsB | Get all cohorts in a given project. Useful for discovering user segments, planning targeted analyses, and retrieving cohort IDs for filtering in other reports. |
| query_retention_reportB | Get data from your Retention reports. Useful for analyzing user engagement over time, measuring product stickiness, and understanding how well your product retains users after specific actions. Only use params interval or unit, not both. |
| custom_jqlB | Run a custom JQL (JSON Query Language) script against your Mixpanel data. Useful for complex custom analyses, advanced data transformations, and queries that can't be handled by standard report types. |
| query_segmentation_sumB | Sum a numeric expression for events over time. Useful for calculating revenue metrics, aggregating quantitative values, and tracking cumulative totals across different time periods. |
| query_profilesB | Query Mixpanel user profiles with filtering options. Useful for retrieving detailed user profiles, filtering by specific properties, and analyzing user behavior across different dimensions. |
| query_frequency_reportC | Get data for frequency of actions over time. Useful for analyzing how often users perform specific actions, identifying patterns of behavior, and tracking user engagement over time. |
| query_segmentation_reportB | Get data for an event, segmented and filtered by properties. Useful for breaking down event data by user attributes, comparing performance across segments, and identifying which user groups perform specific actions. |
| query_segmentation_bucketB | Get data for an event, segmented and filtered by properties, with values placed into numeric buckets. Useful for analyzing distributions of numeric values, creating histograms, and understanding the range of quantitative metrics. |
| query_segmentation_averageB | Averages an expression for events per unit time. Useful for calculating average values like purchase amounts, session durations, or any numeric metric, and tracking how these averages change over time. |
| top_event_propertiesB | Get the top property names for an event. Useful for discovering which properties are most commonly associated with an event, prioritizing which dimensions to analyze, and understanding event structure. |
| top_event_property_valuesB | Get the top values for a property. Useful for understanding the distribution of values for a specific property, identifying the most common categories or segments, and planning further targeted analyses. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 19 tools
Most tools have distinct purposes with clear boundaries, such as query_funnel_report for funnel analysis versus query_retention_report for retention metrics. However, there is some overlap between aggregated_event_property_values and aggregate_event_counts, as both handle aggregated event data but differ in scope (single vs. multiple events), which could cause minor confusion for agents.
Tool names follow a highly consistent verb_noun pattern throughout, such as list_saved_cohorts, query_funnel_report, and get_top_events. All tools use snake_case uniformly, with clear and predictable naming conventions that enhance readability and predictability for agents.
With 19 tools, the count is borderline high for an analytics server, potentially overwhelming for agents to navigate efficiently. While the tools cover a broad range of Mixpanel functionalities, the number approaches the upper limit of what is manageable without clear categorization or grouping.
The tool set provides comprehensive coverage of Mixpanel's core analytics capabilities, including event analysis, segmentation, funnels, retention, and user profiling. There are no obvious gaps; tools like custom_jql even allow for advanced custom queries, ensuring agents can handle a wide variety of data analysis tasks without dead ends.