Team Analytics provides dashboards for team admins and owners to monitor their team's usage of ProdE. The dashboard is organized into four tabs — Overview, Codebase Chat, MCP, and Dashboard Events — each surfacing engagement trends, user activity, and feature adoption through interactive charts and tables.
All tabs support date range filtering and an option to exclude internal users from the data.
Overview
The Overview tab gives you a high-level snapshot of team engagement across all ProdE channels.
| Metric | Type | What It Tells You |
|---|
| Active Users | KPI | Total unique users who interacted with ProdE in the selected period across any channel. |
| Active Users by Channel | Table | Breaks down active users by channel — Codebase Chat, Dashboard, and MCP — so you can see which surfaces your team uses most. |
| Active Users by Date | Time-series chart | Daily trend of unique active users, with lines for each channel. Helps you spot adoption trends and usage dips over time. |
Codebase Chat
The Codebase Chat tab drills into how your team uses ProdE's chat interface for code-related tasks.
KPIs
| Metric | What It Tells You |
|---|
| Active Users | Number of unique users who used Codebase Chat in the selected period. |
| New Chats | Total chat sessions initiated. Each new conversation counts as one chat. |
| Messages | Total user messages sent across all chats. |
Charts
| Chart | Type | What It Tells You |
|---|
| Active Users by Date | Time-series | Daily active user count with breakdown by source (Web, Slack, Jira). Shows where engagement is coming from over time. |
| New Chats by Date | Time-series | Daily count of new chat sessions. Useful for tracking adoption and identifying spikes in usage. |
| Messages by Date | Time-series | Daily message volume. Indicates how deeply your team engages with the chat — more messages per chat signals deeper exploration. |
| Chats by Source | Pie chart | Proportional split of chats by where they were initiated — Web, Slack, or Jira. |
| Messages by Source | Pie chart | Proportional split of messages by source. Helps you understand which platform drives the most conversation volume. |
Tables
| Table | Columns | What It Tells You |
|---|
| Active Users by Source | Source, Count | How many unique users are active on each source (Web, Slack, Jira). |
| Tool Use Counts | Tool Name, Count | Which AI tools are invoked most frequently within chats (e.g., code search, file read). Shows which ProdE capabilities your team relies on. |
| Chats by User | User Name, Email, Count | Per-user breakdown of chat sessions. Identifies your most active users and those who may need onboarding support. |
| Messages by User | User Name, Email, Count | Per-user message counts. A high message count relative to chats indicates deeper, more complex conversations. |
Chat Objective Analysis
ProdE uses an LLM to automatically categorize each chat conversation by its primary intent. This helps you understand what your developers are using the chat for — not just how much they use it.
Objectives are grouped into categories:
| Category | Objectives | What They Mean |
|---|
| Discovery | Find API Endpoint, Find Implementation, Understand Architecture, Understand Data Model, Find Configuration | Developers exploring and navigating the codebase. |
| Planning | Plan Feature, Plan Migration, Design Architecture | Developers using ProdE to plan upcoming work. |
| Development | Write Code Snippet, Write Code Detailed, Debug Issue, Root Cause Analysis | Active coding and debugging tasks. |
| Review | Code Review, Security Review, Performance Analysis | Quality assurance and review activities. |
| Documentation | Create Documentation, Understand Documentation, Onboarding | Documentation-related usage, including new developer onboarding. |
| Testing | Testing Strategy, Write Test Cases | Test planning and generation. |
| External | External Integration, Web Search Info | Looking up external APIs or searching for information. |
| Decision | Compare Approaches, Brainstorming | Evaluating options and ideation. |
You can drill down into any objective to view the individual chats categorized under it, along with summaries of each conversation.
MCP
The MCP tab tracks how your team uses ProdE through IDE integrations (e.g., Claude Code, Cursor, Windsurf). MCP (Model Context Protocol) tool calls represent direct AI-assisted interactions from within the developer's editor.
KPIs
| Metric | What It Tells You |
|---|
| Active Users | Number of unique users who triggered MCP tool calls in the selected period. |
| Tool Calls | Total number of MCP tool calls made. Each invocation of a ProdE tool from the IDE counts as one call. |
Charts
| Chart | Type | What It Tells You |
|---|
| Active Users by Date | Time-series | Daily count of unique users making MCP calls. Tracks IDE integration adoption over time. |
| Tool Calls by Date | Time-series | Daily volume of tool calls. Spikes may correlate with active development sprints or new feature rollouts. |
Tables
| Table | Columns | What It Tells You |
|---|
| Tool Call Breakdown | Tool Name, Count | Which specific MCP tools are used most frequently (e.g., ask_specific_codebase, get_high_level_context). Reveals which ProdE capabilities are most valuable in the IDE workflow. |
| Tool Calls by User | User Name, Email, Count | Per-user tool call counts. Identifies power users and helps gauge individual adoption of IDE integrations. |
Dashboard Events
The Dashboard Events tab tracks how your team interacts with the ProdE web dashboard — including document views and feature engagement.
KPIs
| Metric | What It Tells You |
|---|
| Active Users | Number of unique users who accessed the ProdE dashboard in the selected period. |
| Document Views | Total number of documents viewed across all sources. |
Charts
| Chart | Type | What It Tells You |
|---|
| Document Views by Source | Pie chart | Split of views between Docs Page (browsing documentation directly) and Chat (documents surfaced during a chat conversation). A high "Chat" proportion means developers are discovering docs organically through AI interactions. |
| Document Views by Date | Time-series | Daily document view volume. Helps track documentation engagement trends over time. |