io.github.onetrueclaude-creator/mcp-knowledge-gaps
Find what your knowledge base mentions but doesn't actually explain.
Open source Open in the app JSON README (API)
About
Find what your knowledge base mentions but doesn't actually explain.
Details
- Kind
- MCP servers
- Topic
- AI, RAG & memory
- Publisher
- onetrueclaude-creator
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.0
- Last push
- 2026-04-18T13:37:10Z
- Repository state
- ativo
- Language
- Python
- License
- MIT
- Added
- 2026-08-29 04:00:58
- Updated
- 2026-08-29 04:00:58
- Origin id
io.github.onetrueclaude-creator/mcp-knowledge-gaps
README
<!-- mcp-name: io.github.onetrueclaude-creator/mcp-knowledge-gaps -->
# mcp-knowledge-gaps
**Find what your knowledge base mentions but doesn't actually explain.**
Find concepts mentioned but never defined in your markdown knowledge base
(Obsidian vault, Logseq graph, any folder of .md files). Uses fuzzy
canonicalization to avoid false positives, ranks gaps by frequency ×
region-diversity × novelty, generates prioritized research questions,
and samples from the long tail via sortition to break confirmation
bias in your research queue.
## Install
```bash
pip install mcp-knowledge-gaps
# or
uvx mcp-knowledge-gaps
```
## Usage
### Claude Code
```bash
claude mcp add mcp-knowledge-gaps -- mcp-knowledge-gaps
```
### Claude Desktop
Add to `claude_desktop_config.json`:
```json
{
"mcpServers": {
"knowledge_gaps": {
"command": "uvx",
"args": ["mcp-knowledge-gaps"]
}
}
}
```
## MCP Tools
| Tool | Tier | Description |
|------|------|-------------|
| `find_gaps` | Free | Scan a markdown vault and return concepts mentioned in multiple notes but without their own dedicated note. Applies fuzzy canonicalization and noise filtering. |
| `list_gaps_by_priority` | Free | Return gaps ranked by priority: frequency × diversity × novelty (higher = fill this gap first). |
| `generate_research_questions` | **Pro** | Generate prioritized research questions for the top N gaps. Each question comes with a priority score and factor breakdown. |
| `surprise_research_topic` | **Pro** | Sortition sampling — pick a random gap from the LOW-priority long tail. Breaks confirmation bias by surfacing topics you'd never pick yourself. |
| `export_review_queue` | **Pro** | Export a CSV of top-priority gap concepts, suitable for Anki or other spaced-repetition tools. Writes to output_csv and returns the row count. |
## Pro tier
Unlocks research question generation with RL-weighted ranking, sortition sampling of long-tail gaps, and CSV review queue export.
License activation — any one of these works:
```bash
# 1. Environment variable
export KNOWLEDGE_GAPS_LICENSE="eyJhbGc..."
# 2. CLI flag
mcp-knowledge-gaps --license-key "eyJhbGc..."
# 3. Config file
echo "eyJhbGc..." > ~/.mcp-knowledge-gaps/license.jwt
```
Licenses are verified fully offline — no phone-home, no activation server. Get a license at **https://github.com/onetrueclaude-creator/mcp-knowledge-gaps#pro-tier**.
## Requirements
- Python 3.10+
## License
MIT