# WikiMoth MCP Server

> WikiMoth MCP Server — juliangeymonat-jpg-wikimoth. Use this tool when you need to efficiently retrieve information from a network of linked notes, enabling large language models (LLMs) to answer questions by following authored links without requiring vector databases or additional LLM calls. The WikiMoth MCP Server provides deterministic, token-minimal multi-hop retrieval, allowing for accurate and fast information retrieval. It is ideal for use cases where knowledge is structured as interconnected notes, such as question answering, knowledge graph traversal, and information retrieval tasks.

Canonical page: https://skillsregistry.net/skills/juliangeymonat-jpg-wikimoth  
JSON: https://api.skillsregistry.net/v1/skills/juliangeymonat-jpg-wikimoth

## Description

Enables deterministic, token-minimal multi-hop retrieval from a vault of [[wikilink]] notes via the Model Context Protocol, allowing LLMs to answer questions by following authored links without vector databases or LLM calls during retrieval.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/hrgvzoalvl)
- **Repository:** <https://github.com/juliangeymonat-jpg/wikimoth>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "juliangeymonat-jpg-wikimoth"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/juliangeymonat-jpg-wikimoth` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/juliangeymonat-jpg-wikimoth/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt