wikicapsule

wikicapsule

Turns a git-backed markdown directory into an MCP-compatible knowledge server, enabling any MCP client to read, search, ingest, and maintain a persistent wiki that compounds across sessions.

Category
访问服务器

README

WikiCapsule

A production-grade MCP server implementing Karpathy's LLM Wiki pattern as a shareable, provider-agnostic knowledge capsule.

Python 3.11+ License: MIT MCP

What It Does

WikiCapsule turns a git-backed markdown directory into an MCP-compatible knowledge server. Any MCP client (Claude, Cline, Roo Code, etc.) can read, search, ingest, and maintain a persistent wiki that compounds across sessions.

Key idea: Your knowledge lives in markdown files under version control. The MCP server manages indexing, search, and git operations. The LLM handles reasoning and synthesis. You keep the data.

┌─────────────┐     stdio/SSE     ┌──────────────────┐
│ MCP Client  │ ◄────────────────►│ WikiCapsule MCP  │
│ (Claude)    │                   │ Server           │
└─────────────┘                   │ ┌── Resources    │
                                  │ ├── Tools        │
                                  │ └── Search       │
                                  └────────┬─────────┘
                                           │
                                    ┌──────▼──────┐
                                    │ Git-backed  │
                                    │ Wiki Dir    │
                                    │ (markdown)  │
                                    └─────────────┘

Quick Start

Prerequisites

  • Python 3.11+
  • Git
  • uv (recommended) or pip

Install

# Clone the repository
git clone https://github.com/pisigmac/wikicapsule.git
cd wikicapsule

# Install with uv
uv pip install -e ".[dev]"

# Or with pip
pip install -e ".[dev]"

Initialize a Wiki

# Create a new wiki directory
mkdir my-wiki && cd my-wiki

# Initialize the wiki structure
wikicapsule init

# Or use the MCP server directly
python -m wikicapsule.server --wiki-dir ./my-wiki

Use with Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "wikicapsule": {
      "command": "python",
      "args": ["-m", "wikicapsule.server", "--wiki-dir", "/path/to/my-wiki"]
    }
  }
}

Restart Claude Desktop. You'll see the wiki tools and resources available.

First Ingest

Ask Claude:

"Ingest this article about transformers into my wiki:
[ paste article content ]
"

Or use the CLI:

wikicapsule ingest ./article.md --type article --tags "ml,ai"

Architecture

WikiCapsule follows a layered architecture:

Layer Components Responsibility
MCP Layer server.py, tools.py, resources.py, prompts.py Protocol handling
Wiki Layer wiki.py, markdown.py Directory operations, page CRUD
Search Layer search.py BM25 + vector hybrid search
Storage Layer git_manager.py, SQLite Persistence, version control
Config Layer config.py YAML-driven behavior

Tools (7)

Tool Purpose
wiki_ingest Add a source document to the wiki
wiki_query Query the wiki and find relevant pages
wiki_search Full-text search (BM25/vector/hybrid)
wiki_lint Health-check the wiki
wiki_create_page Create a new wiki page
wiki_update_page Update an existing page
wiki_get_stats Get wiki statistics

Resources

Resource Access
wiki://index.md Catalog of all pages
wiki://log.md Operation log
wiki://WIKI.md Schema documentation
wiki://wiki/{path} Any wiki page
wiki://raw/{path} Any raw source

Prompts (3)

  • ingest_workflow — Guided source ingestion
  • query_workflow — Guided wiki querying
  • lint_workflow — Guided health checking

Search

WikiCapsule uses a hybrid search engine:

  • BM25 via SQLite FTS5 for keyword matching
  • Vector search via sentence-transformers (all-MiniLM-L6-v2)
  • RRF fusion to combine both signals

No API keys. No network calls. Everything stays local.

Directory Structure

my-wiki/
├── .wikicapsule/
│   ├── search.db          # SQLite search index
│   ├── config.yaml        # Server configuration
│   └── lock.json          # Process lock
├── raw/                   # Immutable source documents
│   ├── articles/
│   ├── papers/
│   ├── books/
│   ├── transcripts/
│   └── assets/
├── wiki/                  # LLM-generated markdown
│   ├── index.md           # Auto-maintained catalog
│   ├── log.md             # Auto-maintained log
│   ├── overview.md        # High-level synthesis
│   ├── entities/          # People, orgs, products
│   ├── concepts/          # Ideas, theories
│   ├── sources/           # One page per source
│   ├── comparisons/       # Decision matrices
│   └── explorations/      # Query answers
├── WIKI.md                # Schema documentation
└── .git/                  # Version control

Docker

docker-compose -f docker/docker-compose.yml up

Development

# Run tests
pytest

# Run with coverage
pytest --cov=src/wikicapsule

# Type checking
mypy src/wikicapsule

# Linting
ruff check src/wikicapsule

Documentation

Full documentation is in the docs/ directory:

License

MIT License. See LICENSE for details.

Contributing

Contributions are welcome. See CLAUDE.md and AGENTS.md for how to work with this codebase.

Acknowledgments

  • Andrej Karpathy for the LLM Wiki pattern
  • Anthropic for the Model Context Protocol
  • The sentence-transformers team for local embeddings

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选