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PopNx MCPMCP servers for people who use AI · explained in plain words

Samuraizer

by UladzKhaversion 0.1.7i★ –iRuns on your computer
Registry nameio.github.UladzKha/samuraizer

Install

Overview

Description from the maker (original English)

Local MCP server for meeting transcripts, summaries, action items & decisions — no cloud.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Docs, sheets & office work

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: SAMURAIZER_MEETINGS_DIR

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "samuraizer": {
      "command": "npx",
      "args": [
        "-y",
        "@samuraizer/mcp-server"
      ],
      "env": {
        "SAMURAIZER_MEETINGS_DIR": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add samuraizer --env SAMURAIZER_MEETINGS_DIR=your-value -- npx -y @samuraizer/mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "samuraizer": {
      "command": "npx",
      "args": [
        "-y",
        "@samuraizer/mcp-server"
      ],
      "env": {
        "SAMURAIZER_MEETINGS_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "samuraizer": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@samuraizer/mcp-server"
      ],
      "env": {
        "SAMURAIZER_MEETINGS_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • SAMURAIZER_MEETINGS_DIR env
    Directory containing processed meetings, overriding meetingsDir from the Samuraizer config file. A config file is still required — run 'samuraizer init' first.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account UladzKha, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Docs, sheets & office worki

  • More stars than 0% of the other 3538 servers in this category
  • Published to the registry more recently than 34% of the other 3538 servers in this category
  • Basic checks equal to or better than 100% of the other 3538 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Samuraizer safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.7) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Agent-Native Calendar, Excel MCP Server, MCP Server for Excel

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Runs in the cloud★ 7,620MITUpdated 2 months ago
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Runs on your computer★ 4,276MITUpdated 7 days ago
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Excel automation for AI - Sheets, Power Query, DAX, VBA, Tables, Ranges and more. Windows only.

Runs on your computer★ 847MITUpdated 4 days ago
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

@samuraizer/mcp-server

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.

Tools at a glance

  • `normalize_audio` — Normalize an audio file to 16kHz mono PCM WAV format required by Whisper.
  • `transcribe_audio` — Transcribe an audio file using whisper.cpp. Returns the transcript text.
  • `summarize_transcript` — Generate a concise meeting summary from transcript text.
  • `extract_action_items` — Extract action items from a meeting transcript. Returns a JSON list of tasks with owner and due date.
  • `extract_decisions` — Extract confirmed decisions from a meeting transcript. Returns a JSON list of decisions.
  • `process_recording` — Run the full Samuraizer pipeline on an audio file. Returns summary, action items, decisions, and output file paths.
  • `search_meetings` — Search meetings by summary text, name, action items, and decisions. Transcripts are not searched — use get_meeting for those. Returns ranked results with snippets.
  • `list_meetings` — List all processed meetings, sorted newest first. Optionally limit results.
  • `get_meeting` — Retrieve the full processed output for a specific meeting by id.
  • `meeting://{id}` *(resource)* — A single processed meeting, accessible by its ULID.

Full input and output details for each tool are in Tools below.

The server provides two modes:

  • Query mode — list and retrieve previously processed meetings as structured data or as MCP resources. Read-only, no LLM or audio dependencies needed at runtime.
  • Process mode — run the full Samuraizer pipeline (normalize → transcribe → summarize → extract action items / decisions) on an audio file. Requires the same local toolchain as 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.

Prerequisites

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.

Installation

npm install -g @samuraizer/mcp-server

This exposes a samuraizer-mcp binary on your PATH.

Usage

Run the server directly
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.

Inspect with 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.

Connect from Claude Desktop

Add the server to your Claude Desktop configuration file:

  • macOS / Linux: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or ~/.config/Claude/claude_desktop_config.json (Linux)
  • Windows: %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.

Connect from Claude Code
claude mcp add samuraizer samuraizer-mcp

See the Claude Code MCP documentation for managing MCP servers from the CLI.

Configuration

The MCP server reads the same config file as @samuraizer/cli. There is no separate config for the server itself.

Environment variable overrides

All config fields accept env-var overrides, useful for running the server in different contexts (e.g. one MCP server per project):

VariableMaps to
SAMURAIZER_MEETINGS_DIRmeetingsDir
SAMURAIZER_MODELmodel
SAMURAIZER_OLLAMA_BASE_URLollamaBaseUrl
SAMURAIZER_WHISPER_COMMANDwhisperCommand
SAMURAIZER_WHISPER_MODEL_PATHwhisperModelPath
SAMURAIZER_WHISPER_DEVICEwhisperDevice
SAMURAIZER_WHISPER_PROMPTwhisperPrompt
SAMURAIZER_WHISPER_CARRY_INITIAL_PROMPTwhisperCarryInitialPrompt
SAMURAIZER_LANGUAGElanguage
SAMURAIZER_FFMPEG_COMMANDffmpegCommand
SAMURAIZER_FFPROBE_COMMANDffprobeCommand
SAMURAIZER_LLM_CONCURRENCYllmConcurrency

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.

Tools

Query mode
search_meetings

Search 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.

Query syntax

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_meetings

List 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_meeting

Retrieve 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.

Process mode

These tools run parts of the Samuraizer pipeline on demand. They have local runtime dependencies (whisper-cli, ffmpeg, Ollama).

process_recording

Run 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_audio

Normalize an audio file to 16 kHz mono PCM WAV (the format Whisper expects).

Input: inputPath, outputPath (both required absolute paths).

transcribe_audio

Transcribe an audio file with whisper.cpp.

Input: filePath (required absolute path).

summarize_transcript

Generate a meeting summary from transcript text.

Input: transcriptText (required), model (optional).

extract_action_items

Extract action items from a transcript.

Input: transcriptText (required), model (optional).

extract_decisions

Extract confirmed decisions from a transcript.

Input: transcriptText (required), model (optional).

Resources

The server exposes each processed meeting as an MCP resource:

  • URI scheme: meeting://{id}
  • MIME type: application/json
  • Content: the full meeting JSON, identical to what get_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).

Meeting output format

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:

Troubleshooting

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.

Server starts but list_meetings returns an empty array

Either 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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