Assessment Intelligence

Assessment Intelligence

Psychometric analysis over assessment response data: Item Response Theory for item difficulty / discrimination / guessing, true ability (θ) separate from test difficulty, Cronbach’s alpha reliability, computerized adaptive next-item selection, and Bayesian Knowledge Tracing of skill mastery. Flags items that measure the wrong thing and learners whose trajectory predicts failure.

Registry name	ai.saifs/assessment-intelligence
Endpoint	https://assessment-intelligence-mcp.saifs.ai/mcp
Transport	Streamable HTTP (remote, nothing to install)
Auth	OAuth 2.1 — PKCE (S256) and dynamic client registration; your client opens a sign-in page on first use
Publisher	SAIFS AI

Install

VS Code / GitHub Copilot

Add to .vscode/mcp.json (workspace) or your user mcp.json:

{
  "servers": {
    "saifs-assessment-intelligence": {
      "type": "http",
      "url": "https://assessment-intelligence-mcp.saifs.ai/mcp"
    }
  }
}

Then start the server from the MCP view and sign in when prompted.

Claude (claude.ai, Desktop)

Settings → Connectors → Add custom connector, and paste:

https://assessment-intelligence-mcp.saifs.ai/mcp

Claude Code

claude mcp add --transport http saifs-assessment-intelligence https://assessment-intelligence-mcp.saifs.ai/mcp

Any other MCP client

Point it at https://assessment-intelligence-mcp.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.

Tools

Tool	What it does
analyze_assessment	Per-item IRT parameters (difficulty, discrimination, guessing) and flags for bad items.
estimate_ability	Each student’s true ability (θ), separate from how hard the test was.
assessment_reliability	Cronbach’s alpha and item-total correlations.
adaptive_next_item	Optimal next question from an item bank given current ability (CAT).
track_mastery	Bayesian Knowledge Tracing: P(mastered) per skill over a learning history.
flag_at_risk_learners	Students whose mastery / ability trajectory predicts failure — framed as support, not ranking.
explain_item	Why an item is good or bad, with its fitted parameters.

Example prompts

• “Here’s this mock’s response matrix. Which questions have negative discrimination and should be dropped?”
• “Estimate each student’s true ability on this test, not just their raw percentage.”
• “From this skill history, who is at risk of failing, and what has each person actually mastered?”

Support

• Website: https://saifs.ai
• Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
• Privacy: https://saifs.ai/privacy_policy