ai-wiki-mcp

ai-wiki-mcp

Provides structured search, schema-validated writes, and linting for a markdown knowledge base, enabling agents to operate the wiki over a single streamable-HTTP MCP endpoint.

Category
访问服务器

README

ai-wiki-mcp

A small, wiki-aware MCP server over the ai-wiki/ markdown knowledge base. It replaces the Obsidian + cyanheads/obsidian-mcp-server stack: agents (Claude Code, Hermes, Open WebUI) operate the wiki over a single streamable-HTTP MCP endpoint, while the files stay plain markdown on disk.

Why a purpose-built server instead of a generic filesystem MCP:

  • Structured search — recovers the frontmatter querying Obsidian's dataview gave us (lost on leaving Obsidian).
  • Schema-validated writes — frontmatter crosses the wire as JSON and is emitted as real YAML; the old "['a','b']" array-mangling bug is impossible by construction.
  • Server-side lint — one wiki_lint call runs every structural check instead of dozens of agent round-trips.

Tools

Tool Purpose
wiki_list directory snapshot of notes (optional frontmatter)
wiki_read read a note; full | map | section projections
wiki_search full-text (ripgrep) and/or structured frontmatter filter
wiki_write create/overwrite a note with validated, structured frontmatter
wiki_edit surgical body edit (replace_section/append/replace_text); frontmatter untouched
wiki_set_frontmatter structured frontmatter mutation; taxonomy-checked tags
wiki_lint all structural checks server-side; pure read
wiki_archive move a page to _archive/; report inbound links to fix

Structured search filter DSL

{"type": "concept"}                  # scalar equality
{"confidence": ["high", "medium"]}   # scalar membership
{"tags.contains": "domain/ai"}       # list contains
{"tags.contains_any": [...]}         # list intersects
{"tags.contains_all": [...]}         # list superset
{"missing": ["sources"]}             # keys absent
{"present": ["contested"]}           # keys present

Schema

Validation is driven by <wiki>/.schema.yaml (see .schema.yaml.example), the machine-readable source of truth for frontmatter rules and the tag taxonomy. The human-readable ai-wiki/SCHEMA.md is kept in sync (a wiki_lint drift check guards this). The file is stat-reloaded on change, so adding a domain takes effect without a restart. With no .schema.yaml, validation degrades to warn-not-block.

Configuration

Env Default Meaning
WIKI_ROOT /wiki wiki tree root (bind-mounted)
AI_WIKI_MCP_HOST 0.0.0.0 bind host
AI_WIKI_MCP_PORT 3010 port; endpoint path is /mcp

Metrics

Prometheus metrics are served unauthenticated at GET /metrics on the same AI_WIKI_MCP_PORT as /mcp (no extra port to expose). They cover per-tool call counts/latency/errors plus scrape-time wiki state — file count and size by layer, wikilink totals, broken/ambiguous/orphan links, and lint findings by severity. All metric names are prefixed ai_wiki_mcp_. The wiki gauges are recomputed on each scrape, so keep the scrape interval at 15s or longer.

Web UI

A read-only viewer is served at GET /app on the same AI_WIKI_MCP_PORT as /mcp (no extra port to expose; / redirects to /app/). It gives a navigable file tree, a force-directed link graph (nodes sized by wikilink degree, broken/orphan markers), search (client-side fuzzy jump + server full-text and the frontmatter filter DSL), click-through [[wikilink]] navigation, and markdown rendering with a render⇄source toggle. It is a no-build vanilla-JS SPA reading a small JSON API (/app/api/{tree,page,graph,index,search,stats}); third-party libraries are vendored under static/vendor/ so it works offline. Read-only: no write/edit endpoints are exposed.

Develop

uv sync
uv run pytest          # offline against tests/fixtures/wiki
uv run ruff check src tests
uv run ai-wiki-mcp     # serve (set WIKI_ROOT first)

Docker: docker build -t ai-wiki-mcp . then run with -v <wiki>:/wiki.

Releasing

Versioning is semver, single-sourced from [project].version in pyproject.toml.

  • Every PR must bump the version (major/minor/patch). The version-check workflow fails a PR unless its pyproject.toml version is a valid X.Y.Z strictly greater than main's, so each merge produces a fresh tag. Bump with an edit + uv lock.
  • Merging to main runs the release job (after lint + tests pass): it builds the image, publishes it to the Gitea container registry, and pushes a vX.Y.Z git tag.

Published image (Gitea built-in registry, direct endpoint):

docker pull 192.168.10.32:3000/jhonnold/ai-wiki-mcp:<version>   # or :latest
docker run --rm -p 3010:3010 -v <wiki>:/wiki \
  192.168.10.32:3000/jhonnold/ai-wiki-mcp:<version>

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
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
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
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