Kiwix MCP Server
This server provides offline access to Wikipedia and other ZIM-format content libraries through a local Kiwix instance, enabling AI models to retrieve encyclopedic information without an internet connection.
Search ZIM files (
search_zim): Perform keyword searches within specific ZIM files, with configurable result counts (default: 3).Search with snippets (
search_with_snippets): Search for articles and receive short ~200-character content previews to efficiently assess relevance before fetching full content — the most token-efficient exploration method.Retrieve full article content (
get_content): Fetch complete plain-text article body, with tables, navigation elements, citations, and low-value sections automatically stripped to reduce token usage.Retrieve article summaries (
get_content_summary): Get only the introductory paragraphs of an article (default: 2), offering a token-efficient alternative to full content retrieval.List available ZIM files (
list_all_zims): Discover all ZIM libraries currently loaded in your Kiwix instance (e.g., Wikipedia, Wiktionary), including their names, titles, and descriptions.Offline operation: Works entirely offline once ZIM files are loaded, with multi-ZIM support for accessing different content libraries.
Provides tools for searching and retrieving content from a local Kiwix server, enabling offline access to Wikipedia and other ZIM files through search, full article retrieval, and summaries.
Allows offline querying of Wikipedia content via a local Kiwix server, with capabilities including search, full article retrieval, and token-efficient summaries.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Kiwix MCP ServerGet me a summary of the article about the Moon"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Related MCP server: Kiwix Wiki MCP Server
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 install2. Configure environment
Copy the example environment file and update it for your setup:
cp .env.example .envEdit .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_2026-02Variable | Description | Default |
| URL of your Kiwix server |
|
| Default ZIM file name |
|
Available Tools
search_zim
Search for entries within a specific ZIM file.
Parameters:
Parameter | Required | Description |
| Yes | Search query string |
| No | Target ZIM file (defaults to |
| 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 |
| Yes | Search query string |
| No | Target ZIM file (defaults to |
| 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:
Use
search_with_snippetsto get previews of multiple articlesEvaluate relevance from the snippets (~200 chars each)
Only call
get_contentorget_content_summaryfor 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 |
| Yes | Article title |
| 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 |
| Yes | Article title |
| No | Target ZIM file (defaults to |
| 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
Open LM Studio
Navigate to the MCP Servers settings (under the AI Server tab)
Click + Add MCP Server
Configure:
Name:
kiwix-wikiType:
stdioCommand:
node server.jsWorking 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:
stdioCommand:
node server.jsWorking Directory: Project root
Testing
A comprehensive test harness is included to verify your Kiwix instance is working correctly.
Run All Tests
npm testThis 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 testRunning Manually
For development or testing:
npm startThe 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-toolsProject 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 fileToken Optimization Tips
Automatic Optimizations
Feature | Description | Savings |
Table stripping | Infoboxes, comparison tables, and reference footers are excluded from | 10-30% per article |
Navigation stripping | Sidebars, category links, and navigation elements are removed | ~5-10% per article |
Citation removal | Numeric citation brackets like | ~200-800 tokens per article |
[edit] marker removal | Section heading markers like | ~50-200 tokens per article |
Low-value section removal | "See also", "Further reading", "External links", and "References" sections are stripped from | ~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 | Avoid fetching content for irrelevant articles |
Use | Limit results to the most relevant articles |
Prefer | Get intro paragraphs at ~5% of the token cost |
Specify | 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.

Maintenance
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