Knowledge Graph Mcp

Knowledge Graph MCP Server

An MCP (Model Context Protocol) server for tracking student learning via a knowledge graph. Built with FastMCP, it enables LLMs to build, query, and update a personalized knowledge map with spaced repetition scheduling.

Features

• Knowledge Graph Storage: SQLite-backed graph with concepts as nodes and relationships as edges
• Multi-dimensional Mastery Tracking: Track recall, application, and explanation abilities separately
• Spaced Repetition (SM-2): Automatic scheduling of review sessions based on performance
• Misconception Tracking: Record and query common misconceptions for targeted remediation
• Intelligent Queries: Find knowledge gaps, ready-to-learn concepts, struggling areas
• Mermaid Visualization: Generate visual diagrams of the knowledge graph

Installation

Option 1: Install from Smithery (Recommended)

Install directly via Smithery:

npx @smithery/cli install @zcsabbagh/knowledge-graph-mcp --client claude

Or use the hosted version at: https://smithery.ai/server/@zcsabbagh/knowledge-graph-mcp

Option 2: Install from source

Prerequisites: Python 3.10+

git clone https://github.com/zcsabbagh/knowledge-graph-mcp.gitcd knowledge-graph-mcppip install -e .

Usage

Running the Server

# From the project rootpython -m knowledge_graph_mcp.server

Configure with Claude Code

Add to your Claude Code MCP settings (~/.claude/settings.json):

{  "mcpServers": {    "knowledge-graph": {      "command": "python",      "args": ["-m", "knowledge_graph_mcp.server"],      "cwd": "/path/to/knowledge-graph-mcp"    }  }}

Configure with Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{  "mcpServers": {    "knowledge-graph": {      "command": "python",      "args": ["-m", "knowledge_graph_mcp.server"],      "cwd": "/path/to/knowledge-graph-mcp"    }  }}

MCP Tools

1. add_node

Create a new concept node.

add_node(  concept="Quadratic Formula",  description="Formula for solving ax² + bx + c = 0",  domain="mathematics",  difficulty=0.7,  tags=["algebra", "formulas"])

2. add_edge

Create relationships between concepts.

Relation types:

• prerequisite - Must learn source before target
• builds_on - Target extends source concept
• related_to - Concepts are connected
• contradicts - Common misconception
• applies_to - Application domain
• parent_of - Category hierarchy

add_edge(  source_concept="Algebra",  target_concept="Quadratic Formula",  relation_type="prerequisite")

3. update_node

Update mastery and record reviews. Providing a quality rating (0-5) triggers spaced repetition scheduling.

update_node(  node_id="quadratic_formula",  quality=4,  # SM-2 rating: 0=blackout, 5=perfect  mastery_application=0.6,  misconception_detected="forgets ± sign")

4. query_graph

Intelligent queries for learning insights.

Query types:

• prerequisites - All prerequisites for a concept
• ready_to_learn - Concepts where prereqs are mastered
• due_for_review - Needs review based on schedule
• struggling - High difficulty + low mastery
• stalled - Multiple reviews, no improvement
• misconceptions - Concepts with detected misconceptions
• knowledge_gaps - Low mastery blocking progress
• next_recommended - Best concept to study next

query_graph(query_type="next_recommended", domain="mathematics")

5. read_subgraph

Get the neighborhood around a concept with Mermaid visualization.

read_subgraph(  center_node="calculus",  depth=2,  direction="upstream",  # or "downstream", "both"  output_format="both"   # "json", "mermaid", or "both")

6. get_learning_path

Get ordered prerequisites for a target concept.

get_learning_path(target_concept="calculus")

7. get_statistics

Get learning progress metrics.

get_statistics(domain="mathematics")

How It Works

Data Model

Nodes represent concepts with:

• Mastery levels (overall, recall, application, explanation)
• Spaced repetition data (ease factor, interval, next review date)
• Difficulty rating and review history
• Tags and detected misconceptions

Edges represent relationships with:

• Relation type (prerequisite, builds_on, etc.)
• Strength/confidence rating
• Optional reasoning

Spaced Repetition (SM-2)

When you call update_node with a quality rating:

• 5: Perfect response → longer interval
• 4: Correct with hesitation
• 3: Correct with difficulty
• 2-0: Incorrect → reset interval

The algorithm calculates the next optimal review date based on performance history.

Mastery Calculation

Overall mastery combines dimensional scores:

mastery_level = 0.3 × recall + 0.4 × application + 0.3 × explanation

Storage

Data is stored in SQLite at ~/.knowledge_graph/knowledge.db by default.

Example Workflow

1. LLM discovers student doesn't know "quadratic formula"   → add_node(concept="Quadratic Formula", difficulty=0.7)
2. LLM identifies prerequisites   → add_edge("Algebra", "Quadratic Formula", "prerequisite")
3. Student attempts problem, struggles   → update_node("quadratic_formula", quality=2,                 misconception_detected="confuses ± with +")
4. LLM decides what to teach next   → query_graph("next_recommended")
5. Visualize the learning path   → get_learning_path("quadratic_formula")

License

MIT