mcp-server-llmling
mcp-服务器-llmling
LLMling 服务器手册
概述
mcp-server-llmling 是机器聊天协议 (MCP) 的服务器,它为 LLM 应用程序提供基于 YAML 的配置系统。
LLMLing后端为 LLM 应用程序提供了基于 YAML 的配置系统。它允许设置自定义 MCP 服务器,以提供 YAML 文件中定义的内容。
静态声明:在 YAML 中定义 LLM 的环境 - 无需代码
MCP 协议:基于机器聊天协议 (MCP) 构建,用于标准化 LLM 交互
组件类型:
资源:内容提供商(文件、文本、CLI 输出等)
提示:带有参数的消息模板
工具:LLM 可调用的 Python 函数
YAML 配置创建了一个完整的环境,为 LLM 提供:
通过资源访问内容
结构化提示,实现一致的互动
扩展功能的工具
Related MCP server: MCP YAML API
主要特点
1.资源管理
加载和管理不同类型的资源:
文本文件(
PathResource)原始文本内容(
TextResource)CLI 命令输出(
CLIResource)Python 源代码(
SourceResource)Python 可调用结果(
CallableResource)图像(
ImageResource)
支持资源监视/热重载
资源处理管道
基于URI的资源访问
2.工具系统
将 Python 函数注册并执行为 LLM 工具
支持基于 OpenAPI 的工具
基于入口点的工具发现
工具验证和参数检查
结构化工具响应
3. 及时管理
带有模板支持的静态提示
Python 函数的动态提示
基于文件的提示
提示参数验证
提示论点的完成建议
4. 多种交通选择
基于 Stdio 的通信(默认)
面向 Web 客户端的服务器发送事件 (SSE) / 可流式传输的 HTTP
支持自定义传输实现
用法
使用 Zed 编辑器
在您的settings.json中添加 LLMLing 作为上下文服务器:
{
"context_servers": {
"llmling": {
"command": {
"env": {},
"label": "llmling",
"path": "uvx",
"args": [
"mcp-server-llmling",
"start",
"path/to/your/config.yml"
]
},
"settings": {}
}
}
}使用 Claude Desktop
在claude_desktop_config.json中配置 LLMLing:
{
"mcpServers": {
"llmling": {
"command": "uvx",
"args": [
"mcp-server-llmling",
"start",
"path/to/your/config.yml"
],
"env": {}
}
}
}手动启动服务器
直接从命令行启动服务器:
# Latest version
uvx mcp-server-llmling@latest1. 程序化使用
from llmling import RuntimeConfig
from mcp_server_llmling import LLMLingServer
async def main() -> None:
async with RuntimeConfig.open(config) as runtime:
server = LLMLingServer(runtime, enable_injection=True)
await server.start()
asyncio.run(main())2. 使用自定义传输
from llmling import RuntimeConfig
from mcp_server_llmling import LLMLingServer
async def main() -> None:
async with RuntimeConfig.open(config) as runtime:
server = LLMLingServer(
config,
transport="sse",
transport_options={
"host": "localhost",
"port": 3001,
"cors_origins": ["http://localhost:3000"]
}
)
await server.start()
asyncio.run(main())3.资源配置
resources:
python_code:
type: path
path: "./src/**/*.py"
watch:
enabled: true
patterns:
- "*.py"
- "!**/__pycache__/**"
api_docs:
type: text
content: |
API Documentation
================
...4.工具配置
tools:
analyze_code:
import_path: "mymodule.tools.analyze_code"
description: "Analyze Python code structure"
toolsets:
api:
type: openapi
spec: "https://api.example.com/openapi.json"对于 OpenAPI 架构,您可以安装Redocly CLI来捆绑和解析 OpenAPI 规范,然后再将其用于 LLMLing。这有助于确保正确解析架构引用,并正确格式化规范。如果安装了 redocly,它将自动使用。
服务器配置
该服务器通过 YAML 文件进行配置,包含以下部分:
global_settings:
timeout: 30
max_retries: 3
log_level: "INFO"
requirements: []
pip_index_url: null
extra_paths: []
resources:
# Resource definitions...
tools:
# Tool definitions...
toolsets:
# Toolset definitions...
prompts:
# Prompt definitions...MCP 协议
服务器实现了 MCP 协议,支持:
资源操作
列出可用资源
读取资源内容
监视资源变化
工具操作
列出可用的工具
使用参数执行工具
获取工具架构
快捷操作
列出可用的提示
获取格式化的提示
获取提示参数的完成情况
通知
资源变化
工具/提示列表更新
进度更新
日志消息
Tool Schema Changelog
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No tool schema history has been recorded yet.
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