About Mastery Pathing
Mastery Pathing is a learning service for educators and learners that tracks skill mastery against prerequisites. It identifies gaps and recommends a sequence of topics toward a learning goal.
Key features
- 01Build validated prerequisite graphs
- 02Estimate mastery by skill
- 03Recommend the next topic
- 04Diagnose gaps blocking a goal
- 05Generate mastery-aware sequences
- 06Explain topic recommendations
Use cases
- 01Choose a learner's next topic from a syllabus
- 02Find prerequisites blocking a target topic
- 03Plan a path from current mastery to a goal
MCP support
Exposes prerequisite graph, mastery estimation, gap diagnosis and topic recommendation tools to MCP clients over Streamable HTTP. OAuth 2.1 is used for client sign-in.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mastery-pathing-mcp.saifs.ai/mcpclaude mcp add --transport http ai-saifs-mastery-pathing https://mastery-pathing-mcp.saifs.ai/mcp{"mcpServers":{"ai-saifs-mastery-pathing":{"type":"http","url":"https://mastery-pathing-mcp.saifs.ai/mcp"}}}Open in Cursor{"servers":{"ai-saifs-mastery-pathing":{"type":"http","url":"https://mastery-pathing-mcp.saifs.ai/mcp"}}}Open in VS Code{"mcpServers":{"ai-saifs-mastery-pathing":{"type":"http","url":"https://mastery-pathing-mcp.saifs.ai/mcp"}}}{"mcpServers":{"ai-saifs-mastery-pathing":{"serverUrl":"https://mastery-pathing-mcp.saifs.ai/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEMastery Pathing
The “what should this learner study next?” engine: a validated prerequisite graph, per-skill mastery (Bayesian Knowledge Tracing / Elo), and a knowledge-frontier recommendation. Diagnoses the gaps blocking a goal and returns a mastery-aware sequence from the current state to that goal.
| Registry name | ai.saifs/mastery-pathing |
| Endpoint | https://mastery-pathing-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-mastery-pathing": {
"type": "http",
"url": "https://mastery-pathing-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://mastery-pathing-mcp.saifs.ai/mcp
Claude Code
claude mcp add --transport http saifs-mastery-pathing https://mastery-pathing-mcp.saifs.ai/mcp
Any other MCP client
Point it at https://mastery-pathing-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 |
|---|---|
build_knowledge_graph | Validated prerequisite DAG from topics and dependencies (cycle detection, gap flags). |
estimate_mastery | Per-skill mastery via BKT / Elo across the graph from learner responses. |
next_best_topic | Knowledge frontier: what the learner is ready to learn now (prerequisites met, not yet mastered). |
diagnose_gaps | Prerequisite gaps blocking a target topic, plus a remediation path. |
optimal_sequence | Topological, mastery-aware path from the current state to a goal. |
rate_difficulty | Elo-based item and learner ratings on one scale. |
explain_recommendation | Why this topic next, with mastery evidence. |
Example prompts
- “Here’s our syllabus with prerequisites. What should this student study next?”
- “They want to reach quadratic equations. Which gaps are blocking that, and in what order should we remediate?”
- “Build a learning sequence from their current mastery to the chapter on recursion.”
Support
- Website: https://saifs.ai
- Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
- Privacy: https://saifs.ai/privacy_policy
Comments
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