Action and Predictive analytics

Use this guide to understand what the Action and Predictive tabs do, how each metric is calculated, and what plan is required to unlock each level.

Build absolutely anything...with the right context.

Audience	MCP owners, analytics teams, AI quality stakeholders
Prerequisites	Analytics enabled on your MCP server and enough request volume to generate trend data

Plan requirements

• Core Analytics ($99/month): Core usage and performance cards and charts.
• Core + Action ($149/month): Core plus the Action tab.
• Core + Action + Predictive ($199/month): Core plus Action and the Predictive tab.

See full plan details on MCP Studio pricing and billing and pricing.

What the Action tab does

The Action tab gives source-level evidence for what the AI used and where content quality is costing time:

1. What the AI read for each request:
   • source page URL
   • section/title
   • exact text excerpt passed to the client
   • retrieval confidence score
2. Code snippet provenance:
   • language
   • source page and section
   • originating request context
3. Most-referenced pages and section pressure:
   • which pages are used most
   • which heavily-used sections have low retrieval confidence
4. Action recommendations ranked by expected monthly value.

Impact scoring model

Action business impact is explicitly modeled and shown in the UI:

net_monthly_impact = savings + expansion - rework_cost

• Savings: resolved question chains x time saved x blended hourly rate
• Expansion: grounded, code-backed answer value
• Rework cost: extra requests over healthy threshold x time cost x hourly rate

Each term is surfaced in the dashboard so teams can audit assumptions and adapt them to internal cost models.

What the Predictive tab does

Predictive adds source quality modeling and forward-looking operational forecasting:

1. MCP requests to a working answer (per question chain)
2. Source quality grades (A-D) based on answer efficiency
3. Projected requests-to-answer after content fixes
4. 30-day request and savings forecast with confidence level
5. Ranked content fixes with estimated impact.

Requests-to-working-answer formula

requests_to_working_answer = requests in a question chain until first grounded answer

A question chain is grouped by MCP session ID when available, otherwise by client + rolling time window.
A grounded answer is detected when the top citation's retrieval confidence is high enough to indicate the question was actually answered from your content.

Source quality grading

Sources are graded by average requests-to-answer when that source is involved:

• A (Excellent): 1-4 requests
• B (Good): 5-9 requests
• C (Fair): 10-19 requests
• D (Needs improvement): 20+ requests

Each source row includes:

• grade + label
• average requests to working answer
• unresolved session rate
• weakest location/passages to improve
• recommendation text + projected requests after fixes

How to use Action and Predictive effectively

1. Enable the analytics tier that includes your target tabs.
2. Deploy with analytics enabled in the wizard.
3. Use Action to prioritize the top 1-3 content fixes each week.
4. Re-index sources after edits.
5. Use Predictive to verify that requests to answer trends move down.
6. Track grade changes by source over time and focus on C and D sources first.

Suggested operating cadence

• Weekly: review top actions and apply quick wins
• Monthly: compare forecast vs. actual usage
• Quarterly: reassess plan tier and dashboard scope

Data and privacy notes

• Action/Predictive telemetry is collected per request and displayed only within authenticated server dashboards.
• Stored fields focus on retrieval and quality signals (query text, citations, confidence, session grouping, result counts).
• Excerpts come from customer-indexed source material; no external data is injected into these metrics.
• Follow your internal retention requirements for MCP request logs and source content governance.