lidarr-mcp
MCP server exposing Lidarr's full v1 REST API as tools for reading and managing artists, albums, tracks, queue, history, and more. It enables LLMs to interact with a Lidarr instance via natural language.
README
lidarr-mcp
Part of the arr-mcps collection. MCP server exposing Lidarr's v1 REST API (OpenAPI 3.0.4) as tools, so an LLM can read and manage a Lidarr instance: artists, albums, tracks, track files, the download queue, wanted/missing, history, indexers, import lists, metadata profiles, custom formats, tags, commands, system status, and more. Full surface — reads and writes, with destructive tools flagged.
Built with FastMCP.
Getting an API key
Generate one in Lidarr Settings > General > Security. Auth is the
X-Api-Key header.
Install
Download a wheel from the latest release
and install it as a uv tool (no repo checkout needed):
uv tool install lidarr_mcp-*.whl
This puts a lidarr-mcp command on your PATH. Register it with Claude Code:
claude mcp add lidarr \
--env LIDARR_URL=http://your-lidarr-host:8686 \
--env LIDARR_API_KEY=<key> \
-- lidarr-mcp
From source
uv sync
cp .env.example .env # fill in LIDARR_URL and LIDARR_API_KEY
claude mcp add lidarr \
--env LIDARR_URL=http://your-lidarr-host:8686 \
--env LIDARR_API_KEY=<key> \
-- uv run --directory /path/to/lidarr-mcp lidarr-mcp
Config
| Env var | Required | Default |
|---|---|---|
LIDARR_URL |
yes | - |
LIDARR_API_KEY |
yes* | none (no X-Api-Key header sent if unset) |
* Every API endpoint requires auth; practically you must set it, but the server still starts without one so errors surface from the API rather than at startup.
Tools
15 resource-scoped tools, each covering multiple Lidarr v1 endpoints (223
total) via an operation parameter. Call a tool with operation set to one
of its listed operations and an arguments dict matching that operation's
parameters — the tool's own description (visible to your MCP client) lists
every operation, its signature, and a one-line doc. This keeps the full REST
surface available while costing a fraction of the context budget of
registering all 223 endpoints as separate tools.
| Tool | Operations | Kind |
|---|---|---|
lidarr_media_library |
26 | reads + writes |
lidarr_profiles_formats |
26 | reads + writes |
lidarr_config |
25 | reads + writes |
lidarr_system_commands |
23 | reads + writes |
lidarr_indexers |
23 | reads + writes |
lidarr_notifications_metadata |
18 | reads + writes |
lidarr_tags |
18 | reads + writes |
lidarr_import_lists |
16 | reads + writes |
lidarr_download_clients |
11 | reads + writes |
lidarr_storage |
10 | reads + writes |
lidarr_queue |
9 | reads + writes |
lidarr_history_blocklist |
7 | reads + writes |
lidarr_release_search |
5 | reads + writes |
lidarr_wanted |
4 | read-only |
lidarr_calendar |
2 | read-only |
Example: lidarr_queue(operation="lidarr_delete_queue", arguments={"id": 42}).
Endpoint-level naming (lidarr_<verb>_<resource>) is preserved as the
operation value, so the full endpoint list is still discoverable from each
group tool's description at runtime.
Development
make help # list all commands
| Command | Does |
|---|---|
make sync |
uv sync |
make test |
Offline tests - one per endpoint, mocked HTTP |
make test-integration |
Tests against the live instance (needs LIDARR_URL/LIDARR_API_KEY) |
make build |
Build wheel + sdist into dist/ |
make bump-patch / bump-minor / bump-major |
Bump the version in pyproject.toml + uv.lock |
make clean |
Remove build artifacts |
The release workflow (.github/workflows/release.yml) builds and publishes to
Releases whenever a v*
tag is pushed - so the usual flow is make bump-patch, commit, then tag and
push.
The integration suite is read-only by default (GET endpoints only). Set
LIDARR_WRITE_TESTS=1 to also exercise POST/PUT/DELETE against a scratch tag
(created, updated, then deleted). Never run write tests against a production
library.
推荐服务器
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。
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。
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。
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。
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。
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。
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。
mcp-server-qdrant
这个仓库展示了如何为向量搜索引擎 Qdrant 创建一个 MCP (Managed Control Plane) 服务器的示例。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器