# MCP Analytics

The [**Architect MCP Analytics**](https://alpha.bito.ai/home/dashboard?view=Architect_Overview) dashboard in Bito Cloud provides visibility into how Bito's AI Architect is being used across your workspace through the Model Context Protocol (MCP). Use these metrics to track tool usage, monitor engagement, and understand what tasks the AI Architect is being applied to.

All charts update on a rolling basis and are accessible directly from the Bito UI.

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## Overview

Each time the AI Architect invokes a structured capability — such as code search, file reading, or architecture analysis — it is recorded as a [**tool call**](/ai-architect/integrating-ai-architect-with-your-tools/available-mcp-tools.md). The analytics page aggregates these tool calls to help you answer questions like:

* How much is the AI Architect being used?
* Who is using it, and how many people are actively engaged?
* Which types of tasks is it being used for most?
* Is usage growing or declining over time?

## Available charts

#### Tool Call Volume — Last 4 Weeks

Displays the total number of MCP tool calls made by all users in the last 4 weeks.

Use this as a high-level indicator of overall AI Architect activity. A rising count suggests growing adoption or deeper integration into developer workflows. A sudden drop may point to access issues, a change in team workflow, or a degraded experience worth investigating.

#### Active Users — Last 4 Weeks

Shows the number of unique workspace members who triggered at least one MCP tool call within the last 4 weeks.

This metric reflects the **breadth** of adoption rather than total volume. Compare it alongside tool call volume to understand usage distribution:

* High volume + low active users → a few power users driving most activity
* Growing active users + stable volume → broader but lighter adoption across the team

#### Tool Call Volume — Last 8 Weeks

Breaks down tool call volume **week by week** over the last 8 weeks.

The longer time horizon makes it easier to identify growth trajectories, plateau periods, or regressions following a product change or team event. Use this chart to compare performance before and after AI Architect rollouts, onboarding pushes, or feature updates.

#### Tool Calls by Purpose — Last 4 Weeks

Segments tool calls by their functional category, showing **which types of tasks** the AI Architect is most frequently being used for. Volume is displayed over time so you can track how the purpose distribution shifts within the 4-week window.

Use this chart to identify which Architect capabilities are most valued by your team and where adoption of specific features may need support or enablement.

## Interpreting the data

| Signal                             | What it may indicate                                     |
| ---------------------------------- | -------------------------------------------------------- |
| Steady or growing tool call volume | Healthy adoption and continued use                       |
| Increasing active users            | Broader team engagement                                  |
| Flat volume after onboarding       | Adoption gap; consider enablement efforts                |
| High volume, few active users      | Power-user concentration; limited spread                 |
| Sharp weekly drop                  | Possible access issue or workflow disruption             |
| Single purpose dominating usage    | Narrow use case; other capabilities may be underexplored |

{% hint style="info" %}
**Tip:** Cross-reference Tool Call Volume and Active Users to calculate an average calls-per-user ratio. This helps distinguish between deep individual usage and broad team adoption.
{% endhint %}

## Glossary

| Term                      | Definition                                                                                                                                        |
| ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| **MCP**                   | Model Context Protocol — the structured interface used by AI Architect to invoke tools like code search, file access, and architectural analysis. |
| **Tool call**             | A single invocation of an MCP tool by the AI Architect on behalf of a user. One session may produce many tool calls.                              |
| **Tool call volume**      | The total count of tool calls recorded within a given time window, across all users.                                                              |
| **Active users**          | Unique workspace members who triggered at least one tool call within the specified window.                                                        |
| **Tool calls by purpose** | A categorization of tool calls by the intent or function they serve, such as code navigation or architecture review.                              |
| **Rolling window**        | A time range (e.g. 4 weeks or 8 weeks) that shifts forward each day relative to the current date, rather than being fixed to a calendar period.   |


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