Offline Kiwix Wikipedia MCP Server

An MCP (Model Context Protocol) server that provides offline access to Wikipedia and other ZIM content through a local Kiwix instance.

Overview

This MCP server bridges AI language models (like those in LM Studio) with your local Kiwix server, enabling:

• Offline Wikipedia searches — No internet required once ZIM files are loaded
• Multi-ZIM support — Access Wikipedia, Wiktionary, and other ZIM libraries
• Token-efficient summaries — Get just the intro paragraphs to conserve context window
• Full article retrieval — Pull complete article text when needed

Features

Feature	Description
Search	Search within specific ZIM files
Search with Snippets	Search and get short content previews (~200 chars) for relevance evaluation (token-saving)
Full Content	Retrieve complete article text
Summaries	Get only introductory paragraphs (token-saving)
ZIM Listing	Discover all available ZIM files in your Kiwix instance

Prerequisites

• Kiwix server running with ZIM files loaded
• Node.js 18+ installed
• Local network access to the Kiwix server

Installation

1. Clone and install dependencies

npm install

2. Configure environment

Copy the example environment file and update it for your setup:

cp .env.example .env

Edit .env to point to your Kiwix server:

# Kiwix server address
KIWIX_BASE_URL=http://192.168.1.5:8080

# Default ZIM file (can be overridden per-tool call)
DEFAULT_ZIM=wikipedia_en_all_maxi_2026-02

Variable	Description	Default
KIWIX_BASE_URL	URL of your Kiwix server	http://192.168.1.5:8080
DEFAULT_ZIM	Default ZIM file name	wikipedia_en_all_maxi_2026-02

Available Tools

search_zim

Search for entries within a specific ZIM file.

Parameters:

Parameter	Required	Description
query	Yes	Search query string
zim_file	No	Target ZIM file (defaults to DEFAULT_ZIM)
count	No	Number of results (default: 3)

Example:

{
  "name": "search_zim",
  "arguments": {
    "query": "quantum physics",
    "zim_file": "wikipedia_en_all_maxi_2026-02",
    "count": 5
  }
}

search_with_snippets

Search for articles and return short content snippets (~200 chars) from each result. Use this to evaluate relevance before fetching full content with get_content. This is the most token-efficient way to explore multiple articles at once.

Parameters:

Parameter	Required	Description
query	Yes	Search query string
zim_file	No	Target ZIM file (defaults to DEFAULT_ZIM)
count	No	Number of results (default: 3). Each result includes a ~200 character content snippet.

Example:

{
  "name": "search_with_snippets",
  "arguments": {
    "query": "quantum physics",
    "zim_file": "wikipedia_en_all_maxi_2026-02",
    "count": 3
  }
}

Response format (JSON array):

[
  {
    "title": "Quantum mechanics",
    "snippet": "Quantum mechanics is a fundamental theory in physics that describes the physical properties of nature at the scale of atoms and subatomic particles. It provides a mathematical framework for understanding phenomena...",
    "url": "/wikipedia_en_all_maxi_2026-02/Quantum_mechanics"
  },
  {
    "title": "Quantum field theory",
    "snippet": "Quantum field theory (QFT) is the theoretical framework describing the physics of quantum fields. QFT is used in particle physics and condensed matter physics to construct physical models...",
    "url": "/wikipedia_en_all_maxi_2026-02/Quantum_field_theory"
  }
]

Token-efficient workflow:

1. Use search_with_snippets to get previews of multiple articles
2. Evaluate relevance from the snippets (~200 chars each)
3. Only call get_content or get_content_summary for the most relevant article(s)

get_content

Retrieve the full text content of an article. Tables (infoboxes, comparison tables), navigation elements, images, and reference footers are automatically stripped to minimize token usage while preserving article body text.

Parameters:

Parameter	Required	Description
title	Yes	Article title
zim_file	Yes	Target ZIM file

Example:

{
  "name": "get_content",
  "arguments": {
    "title": "Quantum mechanics",
    "zim_file": "wikipedia_en_all_maxi_2026-02"
  }
}

get_content_summary

Get only the introductory summary of an article (token-efficient).

Parameters:

Parameter	Required	Description
title	Yes	Article title
zim_file	No	Target ZIM file (defaults to DEFAULT_ZIM)
paragraphs	No	Number of paragraphs to return (default: 2)

Example:

{
  "name": "get_content_summary",
  "arguments": {
    "title": "Quantum mechanics",
    "paragraphs": 3
  }
}

list_all_zims

List all ZIM files available in your Kiwix instance.

Parameters: None

Usage in LM Studio

1. Open LM Studio
2. Navigate to the MCP Servers settings (under the AI Server tab)
3. Click + Add MCP Server
4. Configure:
   • Name: kiwix-wiki
   • Type: stdio
   • Command: node server.js
   • Working Directory: Path to this project (e.g., c:\Users\scott\kiwix-wiki-mcp)

The server will start automatically and make the tools available to your AI model.

Usage in Other MCP Clients

This server uses the stdio transport protocol, which is supported by most MCP clients. Configure it with:

• Transport: stdio
• Command: node server.js
• Working Directory: Project root

Testing

A comprehensive test harness is included to verify your Kiwix instance is working correctly.

Run All Tests

npm test

This runs 17 tests covering:

Category	Tests
Connectivity	Server reachability, home page content
ZIM Files	List all available ZIM files (XML/JSON parsing)
Search	Basic search, no results, count parameter, special characters, multi-ZIM
Content	Article retrieval, invalid ZIM handling
Performance	Search response time, content response time
Search with Snippets	Basic functionality, count parameter, empty results, content quality

Test Output

Results are printed to the console and saved to test-results.json:

{
  "timestamp": "2026-06-13T07:42:24.152Z",
  "target": "http://192.168.1.5:8080",
  "defaultZim": "wikipedia_en_all_maxi_2026-02",
  "totalTests": 17,
  "passedTests": 17,
  "failedTests": 0,
  "successRate": "100.0%",
  "results": [...]
}

Custom Configuration

Override defaults via environment variables:

KIWIX_BASE_URL=http://192.168.1.100:8080 DEFAULT_ZIM=my_custom_zim npm test

Running Manually

For development or testing:

npm start

The server runs in stdio mode — it reads from stdin and writes to stdout. To test interactively, you can use an MCP client SDK or the @modelcontextprotocol/cli:

npx @modelcontextprotocol/cli install
npx @modelcontextprotocol/cli list-tools

Project Structure

kiwix-wiki-mcp/
├── .env.example      # Environment template
├── .env              # Your configuration (gitignored)
├── .gitignore        # Git ignore rules
├── server.js         # MCP server implementation
├── test-harness.js   # Test harness for Kiwix connectivity testing
├── test-results.json # Generated test results (JSON format)
├── package.json      # Dependencies and scripts
└── README.md         # This file

Token Optimization Tips

Automatic Optimizations

Feature	Description	Savings
Table stripping	Infoboxes, comparison tables, and reference footers are excluded from get_content, get_content_summary, and search_with_snippets output	10-30% per article
Navigation stripping	Sidebars, category links, and navigation elements are removed	~5-10% per article
Citation removal	Numeric citation brackets like [1], [2][3] are stripped from all content tools	~200-800 tokens per article
[edit] marker removal	Section heading markers like "Quantum mechanics[edit]" are cleaned	~50-200 tokens per article
Low-value section removal	"See also", "Further reading", "External links", and "References" sections are stripped from get_content output	~1,000-4,000 tokens per full article

Recommended Workflow

For the most token-efficient RAG workflow, follow this pattern:

1. search_with_snippets → Get ~200 char previews of N articles (~500-800 tokens total)
2. Evaluate relevance from snippets (no additional tokens consumed)
3. get_content_summary → Fetch only the most relevant article's intro (~200-400 tokens)
4. get_content → Only if full details are needed (~5,000-20,000+ tokens)

Best Practices

Practice	Benefit
Always start with search_with_snippets	Avoid fetching content for irrelevant articles
Use count: 1-3 in search	Limit results to the most relevant articles
Prefer get_content_summary over get_content	Get intro paragraphs at ~5% of the token cost
Specify zim_file explicitly	Avoid searching wrong ZIM files and wasting queries
Use descriptive search queries	More specific queries return more relevant results

License

MIT License. See LICENSE file for details.