Local Model Suitability MCP

io.github.OjasKord由 GitHub 账号 OjasKord 发布v1.1.23更新于 Nov 2, 2026

Check if a task runs locally vs cloud. Save money on calls that don't need cloud inference.

发布方自己的服务：model.tools — Zero-Auth Agent Utilities

已验证Streamable HTTP可网页运行开发者工具 AI 与机器学习

服务详情

标识符: io.github.OjasKord/local-model-suitability-mcp
传输: Streamable HTTP
运行时: Web executable
信任: Verified
发布于: Sep 16, 2026

安全提示

MCP 服务会接收对话中的工具调用请求，并可能执行外部操作。启用前请先了解该服务能做什么，并只授予它所需的权限。

注册中心已验证此条目所属的 GitHub 账号 OjasKord。这并不表明该账号就是产品方。

SourceWeft 的检查只涵盖条目是否完整、端点是否响应；不审查服务器的代码，也不审查其工具的行为。

在 README 中添加徽章

向读者展示该 MCP 服务已收录于 SourceWeft，徽章链接到本页。

[![Listed on SourceWeft](https://sourceweft.com/badge/mcp/io.github.OjasKord%2Flocal-model-suitability-mcp.svg)](https://sourceweft.com/mcp/io.github.OjasKord%2Flocal-model-suitability-mcp?utm_source=readme&utm_medium=badge)

概览

AI 生成的概览

在调用云端模型前判断任务能否改由本地模型处理，返回 LOCAL 或 CLOUD 结论及节省费用估算。

可以做什么: 在发起云端推理前检查任务是否适合本地模型，可指定质量阈值与数据敏感级别。
返回结论、置信度、理由、单次调用预计节省的费用以及推荐的本地模型（如 llama3.2、mistral）。
对机密数据强制给出 LOCAL 结论，使数据不离开本机。
适用场景: 适合代理频繁调用云端推理、希望把简单任务分流到 Ollama、LM Studio 或 llama.cpp 以降低成本的场景。也适用于任务可能含机密数据、需要留在本地处理的情况。
运行要求: 可作为远程 HTTP 端点使用，也可通过 npx 在本地运行（需 Node.js）。本地包需要 ANTHROPIC_API_KEY 用于 Claude 路由分析，README 还提到付费档需 API_KEY。免费档按 IP 统计、无需密钥，付费套餐需另行购买。

安装前请注意

结论由第三方付费服务生成，任务文本会发送给 Anthropic Claude 分析，因此即使结论为 LOCAL，机密内容仍可能离开本机。免费档每月仅 20 次调用且按 IP 统计，付费套餐需花钱。结论仅为成本优化参考，不构成技术建议。

安装

在 SourceWeft 中

打开控制台中的 Local Model Suitability MCP，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.OjasKord%2Flocal-model-suitability-mcp/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "local-model-suitability": {
      "type": "http",
      "url": "https://local-model-suitability-mcp-production.up.railway.app/"
    }
  }
}

README

[smithery badge]

Local Model Suitability MCP

[ToolRank]

Cloud inference is expensive. Everything that can run locally should.

This MCP server tells your agent — before every cloud API call — whether the task can be handled by a local model instead. Route to Ollama, LM Studio, or llama.cpp when you can. Only pay for cloud when you must.

The Tool

`check_local_viability`

Call this BEFORE every cloud inference call. If verdict is LOCAL, skip the cloud call entirely and route to your local model. Only use cloud when this tool returns CLOUD.

Inputs:

Field	Required	Description
`task`	✅	The exact task you are about to send to a cloud model
`quality_threshold`	Optional	`PRODUCTION` (default) / `PROTOTYPE` / `BEST_EFFORT`
`data_sensitivity`	Optional	`PUBLIC` (default) / `INTERNAL` / `CONFIDENTIAL`

CONFIDENTIAL forces LOCAL regardless of task complexity — data never leaves the machine.

Response:

json

{  "verdict": "LOCAL",  "confidence": "HIGH",  "reason": "Simple text summarisation — no reasoning depth required. Any 7B+ local model handles this well.",  "estimated_cost_saving": "$0.002-0.008 saved per call at claude-sonnet pricing",  "recommended_local_models": ["llama3.2:8b", "mistral-7b", "phi3:medium"],  "cloud_justified_reason": null,  "analysis_type": "AI-powered cost routing — NOT a simple lookup"}

{  "verdict": "LOCAL",  "confidence": "HIGH",  "reason": "Simple text summarisation — no reasoning depth required. Any 7B+ local model handles this well.",  "estimated_cost_saving": "$0.002-0.008 saved per call at claude-sonnet pricing",  "recommended_local_models": ["llama3.2:8b", "mistral-7b", "phi3:medium"],  "cloud_justified_reason": null,  "analysis_type": "AI-powered cost routing — NOT a simple lookup"}

Data Sources

AI reasoning: Anthropic Claude (claude-sonnet) — cost routing analysis
No external data sources — pure AI reasoning

Pricing

Subscribe at kordagencies.com

Setup

json

{  "mcpServers": {    "local-model-suitability": {      "command": "npx",      "args": ["-y", "local-model-suitability-mcp"],      "env": {        "ANTHROPIC_API_KEY": "your-key",        "API_KEY": "your-lms-api-key-for-paid-tier"      }    }  }}

{  "mcpServers": {    "local-model-suitability": {      "command": "npx",      "args": ["-y", "local-model-suitability-mcp"],      "env": {        "ANTHROPIC_API_KEY": "your-key",        "API_KEY": "your-lms-api-key-for-paid-tier"      }    }  }}

Free tier requires no API key — tracked by IP.

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

json

{  "mcpServers": {    "local-model-suitability": {      "type": "http",      "url": "https://local-model-suitability-mcp-production.up.railway.app"    }  }}

{  "mcpServers": {    "local-model-suitability": {      "type": "http",      "url": "https://local-model-suitability-mcp-production.up.railway.app"    }  }}

LangChain (Python)

python

from langchain_mcp_adapters.client import MultiServerMCPClientclient = MultiServerMCPClient({    "local-model-suitability": {        "url": "https://local-model-suitability-mcp-production.up.railway.app",        "transport": "http"    }})tools = await client.get_tools()

from langchain_mcp_adapters.client import MultiServerMCPClientclient = MultiServerMCPClient({    "local-model-suitability": {        "url": "https://local-model-suitability-mcp-production.up.railway.app",        "transport": "http"    }})tools = await client.get_tools()

OpenAI Agents SDK (Python)

python

from agents import Agent, HostedMCPToolagent = Agent(    name="Assistant",    tools=[HostedMCPTool(tool_config={        "type": "mcp",        "server_label": "local-model-suitability",        "server_url": "https://local-model-suitability-mcp-production.up.railway.app",        "require_approval": "never"    })])

from agents import Agent, HostedMCPToolagent = Agent(    name="Assistant",    tools=[HostedMCPTool(tool_config={        "type": "mcp",        "server_label": "local-model-suitability",        "server_url": "https://local-model-suitability-mcp-production.up.railway.app",        "require_approval": "never"    })])

LangGraph

Same as LangChain above — langchain-mcp-adapters works with LangGraph natively.

Legal

Results are for cost-optimisation guidance only and do not constitute technical advice. Full terms: kordagencies.com/terms.html

来源：README.md，提交 5e8da9c

工具

check_local_viability

Checks whether a task can be handled by a local model instead of a cloud API. Call this BEFORE your agent routes any task to a cloud model — at the moment a task is ready to be sent and the model has not yet been selected. Use this when your agent is about to make a cloud inference call and wants to determine whether a local model could handle it at lower cost. Returns LOCAL/CLOUD/EITHER verdict with recommended model. If verdict is LOCAL, skip the cloud call entirely — saves cost on every routable inference call. Pass data_sensitivity=CONFIDENTIAL to force LOCAL verdict for tasks that must not leave the machine. Do not route to cloud without checking local viability first.

输入参数task必填data_sensitivityquality_threshold