io.github.zcsabbagh/knowledge-graph-mcp
PYPI · KNOWLEDGE-GRAPH-MCP
Knowledge graph MCP for student learning with spaced repetition and mastery tracking
Available components
Last scanned (35 minutes ago)
Scores higher than 60% of PyPI servers
Category Education and Research
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 15 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency44
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 309 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability69
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1096 tokens (~156/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management77
- Stability observed for 23 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 7 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 5 Nov 26 −18
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.77 functional
- 31 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 30 Oct 26 0
- Stability: 0.97 → pass security
- 29 Oct 26 0
- Stability: pass → 0.97 functional
- 27 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 26 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 23 Oct 26 0
- Stability: 0.97 → pass security
- 22 Oct 26 0
- Stability: pass → 0.97 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed pypi/knowledge-graph-mcp@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 15 packages
| Packages resolved | 15 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
add_edge Add Edge ~188
Create a relationship between two concepts in the knowledge graph.
| Name | Type | Req | Description |
|---|---|---|---|
| reasoning | – | – | Explanation of why this relationship exists |
| relation_type | string | yes | Type of relationship. One of: - "prerequisite": Source must be learned before target - "builds_on": Target extends/deepens source concept - "related_to": Concepts are connected (bidirectional semanti… |
| source_concept | string | yes | Source node ID or concept name |
| strength | number | – | Confidence in the relationship from 0.0 to 1.0. Default 1.0. |
| target_concept | string | yes | Target node ID or concept name |
Structured output declared, but exposes no named fields.
No examples provided.
add_node Add Node ~144
Create a new concept node in the knowledge graph.
| Name | Type | Req | Description |
|---|---|---|---|
| concept | string | yes | Human-readable name for the concept (e.g., "Quadratic Formula") |
| description | – | – | Detailed description of what this concept represents |
| difficulty | number | – | Estimated cognitive load from 0.0 (easy) to 1.0 (hard). Default 0.5. |
| domain | – | – | Category/domain (e.g., "mathematics", "physics", "programming") |
| node_id | – | – | Custom ID for the node. Auto-generated from concept if not provided. |
| tags | – | – | List of categorization tags (e.g., ["algebra", "equations"]) |
Structured output declared, but exposes no named fields.
No examples provided.
get_learning_path Get Learning Path ~82
Get the ordered learning path to reach a target concept. Returns a topologically sorted list of prerequisites, highlighting which concepts the student still needs to learn (gaps).
| Name | Type | Req | Description |
|---|---|---|---|
| include_mastered | boolean | – | Whether to include already-mastered concepts in the path |
| target_concept | string | yes | The goal concept to learn (node ID or concept name) |
Structured output declared, but exposes no named fields.
No examples provided.
get_statistics Get Statistics ~38
Get summary statistics for learning progress.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | – | – | Filter statistics by domain (e.g., "mathematics"). If None, returns all. |
Structured output declared, but exposes no named fields.
No examples provided.
query_graph Query Graph ~201
Query the knowledge graph for learning insights.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | – | – | Filter results by domain (e.g., "mathematics") |
| limit | integer | – | Maximum number of results to return. Default 10. |
| node_id | – | – | Focus node for some queries (required for "prerequisites") |
| query_type | string | yes | Type of query to execute. One of: - "prerequisites": All prerequisites for a concept (requires node_id) - "ready_to_learn": Concepts where all prerequisites are mastered - "due_for_review": Nodes whe… |
Structured output declared, but exposes no named fields.
No examples provided.
read_subgraph Read Subgraph ~169
Get the neighborhood around a concept for context.
| Name | Type | Req | Description |
|---|---|---|---|
| center_node | string | yes | Node ID or concept name to center on |
| depth | integer | – | How many edge hops to traverse (1=direct connections, 2=neighbors of neighbors) |
| direction | string | – | Which edges to follow: - "upstream": Follow edges where center is target (prerequisites) - "downstream": Follow edges where center is source (what it unlocks) - "both": Follow edges in both directions |
| include_mastery | boolean | – | Whether to include mastery data in response |
| output_format | string | – | Output format: - "json": Just the structured data - "mermaid": Just the Mermaid diagram - "both": Both JSON and Mermaid (default) |
Structured output declared, but exposes no named fields.
No examples provided.
update_node Update Node ~243
Update a node's properties and record a review session.
| Name | Type | Req | Description |
|---|---|---|---|
| difficulty | – | – | Update estimated difficulty (0.0-1.0) |
| mastery_application | – | – | Ability to use in new contexts (0.0-1.0) |
| mastery_explanation | – | – | Ability to teach/explain to others (0.0-1.0) |
| mastery_level | – | – | Overall mastery (0.0-1.0). Overrides dimensional calculation. |
| mastery_recall | – | – | Ability to retrieve from memory (0.0-1.0) |
| misconception_detected | – | – | Specific misconception observed (e.g., "confuses ± with +") |
| node_id | string | yes | ID or concept name of the node to update |
| notes | – | – | LLM observations about the student's understanding |
| quality | – | – | SM-2 review quality rating (0-5). Triggers spaced repetition scheduling. - 5: Perfect response - 4: Correct after hesitation - 3: Correct with serious difficulty - 2: Incorrect, but correct answer se… |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the io.github.zcsabbagh/knowledge-graph-mcp server?
io.github.zcsabbagh/knowledge-graph-mcp runs locally as a PyPI package, launched with uvx knowledge-graph-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · knowledge-graph-mcp
claude mcp add zcsabbagh-knowledge-graph-mcp -- uvx knowledge-graph-mcp
What is the io.github.zcsabbagh/knowledge-graph-mcp server?
io.github.zcsabbagh/knowledge-graph-mcp is listed in the public MCP registry as io.github.zcsabbagh/knowledge-graph-mcp. Knowledge graph MCP for student learning with spaced repetition and mastery tracking. This page covers its PyPI package (knowledge-graph-mcp).
Is the io.github.zcsabbagh/knowledge-graph-mcp server safe to use?
io.github.zcsabbagh/knowledge-graph-mcp scores 65 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.zcsabbagh/knowledge-graph-mcp server expose?
io.github.zcsabbagh/knowledge-graph-mcp exposes 7 tools: add_node, add_edge, update_node, query_graph, read_subgraph, and 2 more. Their descriptions and schemas cost roughly 1,065 tokens of context every time the server is loaded.
Is the io.github.zcsabbagh/knowledge-graph-mcp server still maintained?
io.github.zcsabbagh/knowledge-graph-mcp is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.zcsabbagh/knowledge-graph-mcp server under?
io.github.zcsabbagh/knowledge-graph-mcp declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.