de en es ja ko ru zh

mcp-mlx-launcher

Name: mcp-mlx-launcher
Author: globalpocket

by globalpocket

Overview Schema Source Related Servers Discussions Admin

Python

Local

mcp-mlx-launcher

Un servidor MCP (Model Context Protocol) diseñado para gestionar, lanzar y apagar de forma autónoma instancias locales de mlx-lm en entornos Apple Silicon (Mac).

Esta herramienta permite a los agentes de IA (como Cline, Claude Desktop, etc.) iniciar servidores LLM locales bajo demanda, comprobar su estado, preparar entornos y apagarlos correctamente cuando ya no sean necesarios, ahorrando recursos del sistema.

Características

Comprobación del entorno del sistema: Verifica la memoria del sistema y la arquitectura (Apple Silicon) para garantizar la preparación.
Búsqueda y descarga de modelos: Busca modelos MLX disponibles en Hugging Face y los descarga localmente en la caché antes de lanzarlos.
Lanzamiento y gestión de LLMs locales: Inicia, detiene y reinicia un servidor mlx-lm con cualquier modelo compatible en segundo plano.
Comprobación de estado: Verifica si un puerto específico está activo y escuchando actualmente.
Optimizado para Apple Silicon: Creado específicamente para gestionar modelos locales basados en MLX.
Limpieza automática: Limpia y apaga automáticamente todos los procesos LLM gestionados cuando el servidor MCP se desconecta o se apaga, evitando fugas de recursos.

Related MCP server: Local AI MCP

Requisitos previos

macOS (Apple Silicon M1/M2/M3/M4)
Python 3.10 o superior
mlx-lm instalado en su entorno (pip install mlx-lm)

Instalación

# Clone the repository
git clone [https://github.com/YOUR_USERNAME/mcp-mlx-launcher.git](https://github.com/YOUR_USERNAME/mcp-mlx-launcher.git)
cd mcp-mlx-launcher

# Install dependencies
pip install -e .

Uso (Configuración de MCP)

Para utilizar este servidor con su cliente MCP (por ejemplo, Claude Desktop o Cline), añada lo siguiente a su archivo de configuración de MCP:

{
  "mcpServers": {
    "mcp-mlx-launcher": {
      "command": "python",
      "args": [
        "-m",
        "mcp_mlx_launcher.server"
      ]
    }
  }
}

Herramientas disponibles

Una vez conectado, el servidor MCP proporciona las siguientes herramientas al agente de IA:

check_system_environment(): Diagnostica el entorno actual del sistema, devolviendo la memoria unificada disponible (GB) y los detalles de la arquitectura.
check_llm_status(port: int): Devuelve true si un servidor se está ejecutando actualmente en el puerto especificado.
list_running_servers(): Recupera una lista de todos los servidores LLM locales (puertos y modelos) que se ejecutan actualmente en segundo plano.
search_mlx_models(search_query: str = "", limit: int = 10): Busca modelos en formato MLX disponibles en Hugging Face y enumera sus detalles (como el recuento de descargas y el ID del modelo).
download_model(model_name: str): Pre-descarga un modelo MLX especificado desde Hugging Face y lo almacena en caché localmente. Útil para preparar modelos grandes antes de lanzarlos.
launch_llm_server(model_name: str, port: int, memory_requirement_gb: float = 4.0): Lanza una instancia de mlx_lm.server en segundo plano. Incluye una comprobación opcional de requisitos de memoria para evitar errores de falta de memoria.
restart_llm_server(port: int, model_name: str = None, memory_requirement_gb: float = 4.0): Detiene correctamente el servidor en ejecución en el puerto dado y lo reinicia. Si se omite model_name, se reinicia con el modelo cargado actualmente.
shutdown_llm_server(port: int): Termina correctamente el servidor LLM en ejecución en el puerto dado.

Available Tools

8 tools

check_llm_statusB

指定されたポートでサーバーがリッスンしているか（稼働中か）を確認します。

ParametersJSON Schema

Name	Required	Description	Default
`port`	Yes	確認するポート番号

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description does not disclose what the tool returns (e.g., boolean, status message) or any side effects. Without annotations, the agent lacks behavioral context beyond the basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no unnecessary words, perfectly front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool with one parameter and no output schema, the description is adequate but lacks mention of return format or how it differs from list_running_servers. It could be more complete for optimal agent decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Parameter schema coverage is 100%, and the description adds minimal value beyond the schema's parameter description. The baseline of 3 applies since the schema already documents the port parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it checks if a server is listening on a specified port, using a specific verb and resource. It distinguishes from sibling tools like launch_llm_server or shutdown_llm_server by focusing on status checking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as list_running_servers or check_system_environment. There is no mention of prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

check_system_environmentA

現在のシステム環境（Apple Siliconか、空きメモリが何GBあるかなど）を診断します。

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the tool's purpose (diagnose system environment) but does not mention side effects, permissions, or whether it is destructive. The diagnostic nature implies a read-only operation, but this is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded with the main action ('診断します'). It is concise and contains no unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description adequately conveys the tool's purpose for a simple diagnostic tool. However, it does not explain the output format or return value, which is a gap given the lack of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, and schema coverage is 100% trivially. Per guidelines, baseline score is 4. The description adds context about the type of diagnostic information, which is helpful.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states it diagnoses the current system environment, specifically mentioning Apple Silicon and free memory. This clearly distinguishes it from sibling tools that deal with LLM status, servers, and models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or alternatives are provided. However, the context of sibling tools and the description implies it should be used to check system hardware before running models. This is adequate but lacks explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

download_modelA

Hugging Faceから指定されたMLXモデルを事前にダウンロードし、ローカルにキャッシュします。大きなモデルの起動前の準備に利用します。

ParametersJSON Schema

Name	Required	Description	Default
`model_name`	Yes	ダウンロードするモデル名 (例: mlx-community/Llama-3-8B-Instruct-4bit)

TDQS

A3.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided; the description discloses caching but fails to mention overwrite behavior, authentication needs, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with clear front-loading of the action and purpose, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple single-parameter tool, the description covers the core action and context, though additional details on caching behavior would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the description adds no extra meaning beyond the schema's parameter description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool downloads MLX models from Hugging Face and caches locally, distinguishing it from sibling tools like launch_llm_server or search_mlx_models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It mentions 'use to prepare before launching large models' which implies when to use, but no explicit exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

launch_llm_serverB

mlx_lm.server をサブプロセスとしてバックグラウンドで起動します。空きメモリが少ない場合は起動が拒否されます。

ParametersJSON Schema

Name	Required	Description
`model_name`	Yes	起動するモデル名 (例: mlx-community/Llama-3-8B-Instruct-4bit)
`port`	Yes	サーバーを起動するポート番号
`memory_requirement_gb`	No	起動に必要な空きメモリの目安(GB)。未指定時はデフォルトで 4.0GB。

TDQS

B3.4/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions background subprocess and memory rejection, but lacks details on return behavior, readiness confirmation, error handling, or side effects. This is insufficient for a launch tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short, front-loaded sentences with no unnecessary words. Each sentence provides essential information concisely.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, so the description should explain return values or status. It only describes the action and a rejection condition, but not success behavior, how to check status, or how to interact with the launched server. Incomplete for a start operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters. The description adds no extra parameter meaning beyond what is in the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it launches mlx_lm.server as a background subprocess, a specific verb+resource. It also mentions a condition for rejection (low memory). This distinguishes it from siblings like check_llm_status or list_running_servers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for initial server launch, but does not explicitly state when to use vs alternatives (e.g., restart_llm_server for restarting). No explicit exclusions or prerequisites beyond memory.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_running_serversA

現在バックグラウンドで稼働しているすべてのローカルLLMサーバー（ポート番号とモデル名）の一覧を取得します。

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It confirms the tool is read-only but does not disclose potential behavioral traits like empty list behavior, authentication needs, or performance aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is concise, front-loaded, and contains no unnecessary information. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and lack of output schema, the description provides adequate context: what is returned (port and model name). It could mention handling of no servers running, but overall it is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so the description does not need to add parameter information. A baseline of 4 is appropriate as the description is clear about the lack of parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the action (retrieve list), resource (running local LLM servers), and output details (port numbers and model names). It distinguishes itself from sibling tools like launch_llm_server or shutdown_llm_server by focusing on listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or when-not-to-use guidance is provided. The use case is implied by the tool name and description, but no alternatives or exclusions are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

restart_llm_serverB

指定されたポートで稼働しているサーバーを一度停止し、再起動します。モデルの切り替えなどにも使用できます。

ParametersJSON Schema

Name	Required	Description
`port`	Yes	再起動するサーバーのポート番号
`model_name`	No	（オプション）新しく起動するモデル名。省略した場合は現在そのポートで稼働しているモデルをそのまま再起動します。
`memory_requirement_gb`	No	（オプション）起動に必要な空きメモリの目安(GB)。未指定時はデフォルトで 4.0GB。

TDQS

B3.3/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not disclose behavioral traits such as service disruption, session handling, or error states. For a destructive operation, this is insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that gets to the point. However, it could be more informative without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description should provide more context about what happens during restart, prerequisites (e.g., server must be running), and potential impacts. It falls short.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema covers 100% of parameters with clear descriptions. The tool's description adds no additional meaning beyond the schema, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that it stops and restarts a server on a specified port, and mentions it can be used for switching models. This aligns with the name and distinguishes it from siblings like 'shutdown_llm_server' and 'launch_llm_server'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for model switching but does not explicitly state when to use versus alternatives. No guidance on when not to use or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_mlx_modelsC

Hugging Faceからダウンロード可能なMLXフォーマットのLLMモデルを検索・リストアップします。

ParametersJSON Schema

Name	Required	Description	Default
`search_query`	No	検索キーワード（例: 'llama', 'qwen'）。未指定の場合は人気のMLXモデルを返します。
`limit`	No	取得する最大件数。デフォルトは10。

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, rate limits, pagination, or result format. The minimal description leaves substantial gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no irrelevant information, achieving conciseness. However, it may be slightly under-specified for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and full schema coverage, the description is adequate but could mention that the search is read-only and limited to MLX format. It does not explain return values or pagination.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and already documents both parameters. The description adds minimal extra value by providing an example for search_query and stating the default for limit, but does not go beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches and lists MLX format LLM models from Hugging Face, which distinguishes it from sibling tools focused on server management and model downloading.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool over alternatives like download_model, or any prerequisites. Usage context is implied but not explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

shutdown_llm_serverA

指定されたポートで稼働しているローカル LLM サーバープロセスを安全に終了させます。

ParametersJSON Schema

Name	Required	Description	Default
`port`	Yes	終了させるサーバーのポート番号

TDQS

A3.7/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions 'safe termination' but lacks detail on side effects (e.g., data loss), required permissions, or confirmation feedback. No annotations are provided, so the description partially covers behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with action and target. No superfluous words, highly efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, the description is mostly adequate but omits what happens after termination (e.g., response, confirmation) and any prerequisites. With no output schema, more detail would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the parameter description already explains the port number. The tool description adds no additional semantic value beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (terminate), the target (local LLM server process), and the condition (running on specified port). It distinguishes well from sibling tools like launch_llm_server (start) and restart_llm_server (stop+start).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the tool name and description, but no explicit when-to-use or when-not-to-use guidance is provided. Sibling tools exist for related actions (e.g., restart), but no alternatives are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

8 tool updatesv0.2.1Apr 30, 2026
- First observedcheck_llm_status
- First observedcheck_system_environment
- First observeddownload_model
- First observedlaunch_llm_server
- First observedlist_running_servers
- First observedrestart_llm_server
- First observedsearch_mlx_models
- First observedshutdown_llm_server

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored 2026-05-06 10:55 across 8 tools

Disambiguation5/5

Each tool targets a distinct operation: system check, model download, server launch, list, restart, search, shutdown, and status check. No two tools have overlapping purposes.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_llm_status, launch_llm_server, shutdown_llm_server). No mixing of conventions.

Tool Count5/5

8 tools is well-scoped for an LLM server launcher covering environment check, model management, and server lifecycle. Neither too few nor too many.

Completeness4/5

Covers the full lifecycle: system check, model search/download, launch, list, restart, shutdown. Minor gap in lacking detailed server logs or configuration updates, but core operations are complete.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

hithereiamaliff-mcp-nextcloudOAuth
ai.smithery
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Cloud Storage File Systems Workplace & Productivity
238
mcp-aichatOAuth
io.github.AceDataCloud
MCP server for AI dialogue using various LLM models via AceDataCloud
AI & Machine Learning
2
mcp A
ai.llmse
Public MCP server for the LLM Search Engine
Search RAG Systems Developer Tools
102
supportsheep
io.github.pooriaarab
Remote MCP server for supportsheep: run AI interviews and manage support content for your blog.
Customer Support Content Management Systems Marketing
1

Related MCP Servers

local-mmcp
Image & Video Processing Audio Processing App Automation
rorojiao
F
license
A
quality
D
maintenance
Local multimodal MCP server for vision, document, audio, video, and GUI automation, all processed locally on Apple Silicon for privacy and security.
Updated 3 months ago (2026-06-10 12:34 UTC)
9
-
Local AI MCPofficial
AI & Machine Learning Developer Tools
TMHSDigital
A
license
A
quality
A
maintenance
Unified MCP server for managing local model runtimes (Ollama, LM Studio, etc.), enabling provider-agnostic discovery, lifecycle management, hardware-fit checks, and delegated inference.
Updated 6 days ago (2026-09-14 06:23 UTC)
16
30 npm
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
kokoro-tts-mcp
Text-to-Speech Audio Processing
scottschram
A
license
Not graded
quality
C
maintenance
Text-to-speech MCP server using the Kokoro-82M model accelerated with MLX on Apple Silicon, enabling local Claude and Codex clients to speak text aloud and convert text to audio.
Updated 3 months ago (2026-07-03 16:53 UTC)
4
MIT
apple-fm-mcp
AI & Machine Learning Text Summarization
yihan2099
A
license
Not graded
quality
D
maintenance
MCP server that enables local Apple on-device Foundation Model access via any MCP client, supporting text generation, structured output, and multi-turn chat on macOS.
Updated 7 months ago (2026-02-26 08:42 UTC)
2
MIT

mcp-mlx-launcher

Características

Requisitos previos

Instalación

Uso (Configuración de MCP)

Herramientas disponibles

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Local AI MCPofficial