Agent-Native Calendar
Agent-Native Google Calendar - manage events, sync, and public booking
作者提供的说明(英文原文)
Local MCP server for meeting transcripts, summaries, action items & decisions — no cloud.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
SAMURAIZER_MEETINGS_DIR你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"samuraizer": {
"command": "npx",
"args": [
"-y",
"@samuraizer/mcp-server"
],
"env": {
"SAMURAIZER_MEETINGS_DIR": "your-value"
}
}
}
}在终端中运行这条命令
claude mcp add samuraizer --env SAMURAIZER_MEETINGS_DIR=your-value -- npx -y @samuraizer/mcp-server运行前请先填写空着的值。
粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"samuraizer": {
"command": "npx",
"args": [
"-y",
"@samuraizer/mcp-server"
],
"env": {
"SAMURAIZER_MEETINGS_DIR": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"samuraizer": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@samuraizer/mcp-server"
],
"env": {
"SAMURAIZER_MEETINGS_DIR": "your-value"
}
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
SAMURAIZER_MEETINGS_DIR env这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 UladzKha 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.7)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Agent-Native Calendar, Excel MCP Server, MCP Server for Excel
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Agent-Native Google Calendar - manage events, sync, and public booking
Create and edit Excel files with AI: charts, PivotTables, formulas. No Excel needed.
Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Local-first MCP server that gives AI agents access to your meeting recordings — transcripts, summaries, action items, and decisions — without sending anything to the cloud.
Model Context Protocol server for Samuraizer — exposes processed meetings to AI agents (Claude Desktop, Claude Code, MCP Inspector, etc.) over stdio.
get_meeting for those. Returns ranked results with snippets.Full input and output details for each tool are in Tools below.
The server provides two modes:
samuraizer itself: whisper-cli, ffmpeg, ffprobe, and a running Ollama instance.Everything stays local. The server reads meetings from a directory on your machine and never sends data to any external service.
This package depends on @samuraizer/cli for its config and pipeline. Before using the MCP server, install and initialize the CLI:
npm install -g @samuraizer/cli
samuraizer init
Edit the generated config (path printed by samuraizer config path) and make sure meetingsDir points to the directory where your processed meetings live. The MCP server reads from that same directory.
For process mode, you additionally need the Samuraizer runtime dependencies installed and configured: whisper-cli from whisper.cpp, ffmpeg / ffprobe, and a running Ollama server with a pulled model. See the main Samuraizer README for setup details.
For query mode only (list_meetings, get_meeting, meeting:// resources), none of the runtime dependencies are required — just a populated meetingsDir.
npm install -g @samuraizer/mcp-server
This exposes a samuraizer-mcp binary on your PATH.
samuraizer-mcp
The server speaks MCP over stdio. It is not meant to be used interactively from a shell — it is intended to be launched by an MCP client such as Claude Desktop or MCP Inspector.
npx @modelcontextprotocol/inspector samuraizer-mcp
Opens a browser UI where you can list tools and resources, call them with arguments, and see responses. Useful for debugging.
Add the server to your Claude Desktop configuration file:
~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or ~/.config/Claude/claude_desktop_config.json (Linux)%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": { "samuraizer": { "command": "samuraizer-mcp" } } }
If samuraizer-mcp is not found by Claude Desktop (PATH issues are common with version managers like fnm, nvm, or volta), use an absolute path instead. Find it with which samuraizer-mcp, then:
{
"mcpServers": { "samuraizer": { "command": "/absolute/path/to/samuraizer-mcp" } } }
You can also override the meetings directory per-server via environment variables:
{
"mcpServers": { "samuraizer-personal": { "command": "samuraizer-mcp", "env": { "SAMURAIZER_MEETINGS_DIR": "/Users/me/personal-meetings" } }, "samuraizer-work": { "command": "samuraizer-mcp", "env": { "SAMURAIZER_MEETINGS_DIR": "/Users/me/work-meetings" } } } }
Restart Claude Desktop after editing the config.
claude mcp add samuraizer samuraizer-mcp
See the Claude Code MCP documentation for managing MCP servers from the CLI.
The MCP server reads the same config file as @samuraizer/cli. There is no separate config for the server itself.
All config fields accept env-var overrides, useful for running the server in different contexts (e.g. one MCP server per project):
| Variable | Maps to |
|---|---|
SAMURAIZER_MEETINGS_DIR | meetingsDir |
SAMURAIZER_MODEL | model |
SAMURAIZER_OLLAMA_BASE_URL | ollamaBaseUrl |
SAMURAIZER_WHISPER_COMMAND | whisperCommand |
SAMURAIZER_WHISPER_MODEL_PATH | whisperModelPath |
SAMURAIZER_WHISPER_DEVICE | whisperDevice |
SAMURAIZER_WHISPER_PROMPT | whisperPrompt |
SAMURAIZER_WHISPER_CARRY_INITIAL_PROMPT | whisperCarryInitialPrompt |
SAMURAIZER_LANGUAGE | language |
SAMURAIZER_FFMPEG_COMMAND | ffmpegCommand |
SAMURAIZER_FFPROBE_COMMAND | ffprobeCommand |
SAMURAIZER_LLM_CONCURRENCY | llmConcurrency |
Env vars override values from the config file. Booleans accept 1/0, true/false, yes/no, or on/off.
llmConcurrency defaults to 1, so process_recording runs summary, action-item, and decision extraction sequentially on all hardware. Values 2 and 3 explicitly opt into parallel Ollama requests and require enough spare VRAM for additional KV-cache slots.
search_meetingsSearch processed meetings by summary text, meeting name, action items, and decisions.
Transcripts are deliberately not searched — they are one to two orders of magnitude larger than the fields above, and scanning them on every query would not scale. Use get_meeting when you need the transcript.
Input:
query *(string, required)* — search query, case-insensitive. See Query syntax.limit *(integer, optional)* — maximum results to return (default 10).Returns: JSON array of ranked results, each with id, name, generated_at, duration_sec, score, matched_in (any of summary, name, action_items, decisions), and a snippet around the best match. Results are ranked by how many fields matched — a summary hit weighs more than a name, action-item, or decision hit — with ties broken by recency. Useful for finding a specific meeting without enumerating all of them.
The query is split into words, and a meeting matches only when every word is found. The words do not have to share a field, so patient id post log matches a meeting whose summary mentions the patient ID and whose action items mention the post log.
Each word matches as a substring, so export finds exporter.
Wrap the query in double quotes to require an exact phrase instead:
patient id post log → all four words, anywhere in the searched fields
"patient id post log" → that exact run of characters, in one field
A meeting containing the whole query verbatim scores above one that merely scatters the same words, so exact matches still surface first.
list_meetingsList processed meetings, sorted newest first.
Input:
limit *(integer, optional)* — maximum number of results to return.Returns: JSON array of meeting summaries (id, name, generated_at, summary_preview, participant_count, duration_sec).
get_meetingRetrieve the full processed output for a specific meeting.
Input:
id *(string, required)* — the meeting ID (ULID).Returns: the full meeting JSON, including transcript, summary, action items, decisions, and provenance metadata. Returns an error if the meeting is not found.
These tools run parts of the Samuraizer pipeline on demand. They have local runtime dependencies (whisper-cli, ffmpeg, Ollama).
process_recordingRun the full pipeline on an audio file. Recommended entrypoint for processing recordings via an agent.
Input:
filePath *(string, required)* — absolute path to the audio file.model *(string, optional)* — Ollama model to use; falls back to the configured default.Returns: JSON with summary, action items, decisions, and output file paths.
normalize_audioNormalize an audio file to 16 kHz mono PCM WAV (the format Whisper expects).
Input: inputPath, outputPath (both required absolute paths).
transcribe_audioTranscribe an audio file with whisper.cpp.
Input: filePath (required absolute path).
summarize_transcriptGenerate a meeting summary from transcript text.
Input: transcriptText (required), model (optional).
extract_action_itemsExtract action items from a transcript.
Input: transcriptText (required), model (optional).
extract_decisionsExtract confirmed decisions from a transcript.
Input: transcriptText (required), model (optional).
The server exposes each processed meeting as an MCP resource:
meeting://{id}application/jsonget_meeting returns.Listing resources returns one entry per meeting in meetingsDir. Reading a resource returns its full content. Resources are particularly useful for clients that surface them in their UI (e.g. Claude Desktop's resource picker).
All meeting JSON conforms to the open Samuraizer meeting-output schema, which describes meeting structure, transcript segments, summaries, action items, decisions, and provenance metadata.
The schema and its specification live in the memnex-spec package:
spec/meeting-output.schema.jsonspec/SPEC.mdspec/examples/Error: The config file is not created, please run 'samuraizer init'The MCP server uses the same config as the CLI. Install @samuraizer/cli and run samuraizer init first.
list_meetings returns an empty arrayEither meetingsDir is empty, or all meeting.json files in it failed schema validation. The server logs structured warnings to stderr for each invalid meeting it skips — check the terminal where the MCP server is running (or the Claude Desktop logs at `~/Library/Logs/Claude/mcp-serv
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