OllamaSharp 本地sse call MCP 快速体验示例

.net 9 

ollama 测试模型 qwen3 4B

Server 端依赖包  ModelContextProtocol.AspNetCore (0.3.0-preview.4)

客户端依赖包　　ModelContextProtocol.AspNetCore (0.2.0-preview.3),ModelContextProtocol.Core (0.2.0-preview.3),  OllamaSharp , OllamaSharp.ModelContextProtocol

本示例 Ollama包5.3.6 

客户端使用低版本 ModelContextProtocol 是因为ollama最新包5.3.6中调用工具需要使用到ModelContextProtocol.Core.Protocol.CallToolResponse的类.这个类在0.2.0-preview.3版本后被移除.使用CallToolResult 替换.

 

本地模型建议 1.5B以上. 1.5B的大部分模型不支持直接tools把模型传过去. 但可以要求按指定格式生成调用.再解析.不过1.5B 生成跟抽风一样一会好一会坏.但我在4B里测了很多次都是很稳定的.

服务端工具代码

using System.ComponentModel;
using ModelContextProtocol.Server;

namespace test_server.tools;

[McpServerToolType]
public class FileTool
{
    [McpServerTool(Name = "文件查找"), Description("在指定的目录中查找文件")]
    public string FindFileInDirectory([Description("查找文件的基础目录")] string baseDirectory
    , [Description("需要查找的文件名")] string fileName)
    {
        try
        {
            if (!Directory.Exists(baseDirectory))
            {
                return $"查询的目录{baseDirectory}不存在!";
            }
            var fs = Directory.GetFiles(baseDirectory, $"*{fileName}*", SearchOption.AllDirectories);
            if (fs.Length == 0)
            {
                return $"在目录{baseDirectory}中没有找到文件名包含{fileName}的任何文件!";
            }
            return $"在目录{baseDirectory}中找到文件名包含{fileName}的文件{fs.Length}个 :\r\n{string.Join("\r\n", fs)}";
        }
        catch (System.Exception ex)
        {
            Console.WriteLine(ex.Message + "\n" + ex.StackTrace);
            return ex.Message;
        }
    }
}

服务端启动代码(Program.cs)

using Microsoft.AspNetCore.Builder;
using Microsoft.Extensions.DependencyInjection;


var builder = WebApplication.CreateBuilder(args);
builder.Services.AddMcpServer()
    .WithHttpTransport()
    .WithToolsFromAssembly();
var app = builder.Build();

app.MapGet("/run", () => "Hello MCP Server!");

app.MapMcp();
app.Run();

 

客户端调用(Program.cs)

var sseTran = new SseClientTransport(new SseClientTransportOptions
{
    Name = "Test Tools",
    Endpoint = new Uri("http://localhost:5000")

});

await using var mcpClient = await McpClientFactory.CreateAsync(sseTran);
var mcpTools = await mcpClient.ListToolsAsync();

//List<OllamaSharp.Models.Chat.Tool> tools = GetOllamaToolsFromMCPTools(mcpTools);
var tools = new List<OllamaSharp.ModelContextProtocol.Server.McpClientTool>();

foreach (var tool in mcpTools)
{
    Console.WriteLine($"{tool.Name} ({tool.Description})");
    tools.Add(new OllamaSharp.ModelContextProtocol.Server.McpClientTool(tool, mcpClient));
}

HttpClient hc = new HttpClient();
// set up the client
hc.Timeout = TimeSpan.FromMinutes(5);
hc.BaseAddress = uri;
//这里未必需要httpClient.因为我本地模型生成比较慢需要设个超时,所以才用了它
var ollama = new OllamaApiClient(hc, "qwen3:4b");
ollama.Config.Uri = uri;

var chatOptions = new ChatOptions()
{
    Tools = [.. mcpTools],
    Temperature = 0.6f,
    AllowMultipleToolCalls = true,
    ToolMode = ChatToolMode.Auto,
    MaxOutputTokens = 256,//这个不设置 超级多废话(我当前用的是qwen3 4B)

};

var chat = new Chat(ollama);
while (true)
{

 System.Console.InputEncoding = System.Text.Encoding.UTF8;
    Console.Write("User >");
    string content = Console.ReadLine();
    if (String.IsNullOrWhiteSpace(content))
    {
        continue;
    }
    Console.Write("AI >");
    await foreach (var answerToken in chat.SendAsync(content, tools))
        Console.Write(answerToken);
}