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Ollama Integration with MCP

This guide shows how to integrate MCP (Model Context Protocol) servers with Ollama to enable function calling capabilities with local language models.

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Overview

The Ollama integration allows you to:
  • Connect to MCP servers and expose their tools to Ollama models
  • Use local LLMs with function calling capabilities
  • Build interactive chat applications with tool support

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Prerequisites

  • Ollama installed and running locally
  • Node.js 16+ (for TypeScript implementation)
  • Python 3.13+ (for Python implementation)
  • An MCP server running (e.g., the Game of Thrones quotes server)

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TypeScript Implementation

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Installation

First, install the required dependencies:
package.json
{
  "name": "ollama-ts-app",
  "version": "1.0.0",
  "type": "module",
  "dependencies": {
    "@modelcontextprotocol/sdk": "^1.8.0",
    "node-fetch": "^3.3.2"
  },
  "devDependencies": {
    "@types/node": "^22.13.13",
    "typescript": "^5.8.2"
  }
}

npm install

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MCP Client Setup

Create a reusable MCP client to connect to MCP servers:
src/mcpClient.ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";

export class MCPClient {
  private serverParams: {
    command: string;
    args: string[];
    env?: Record<string, string>;
  };
  private client: Client | null = null;
  private transport: StdioClientTransport | null = null;

  constructor(
    command: string,
    args: string[],
    env?: Record<string, string>
  ) {
    this.serverParams = { command, args, env };
  }

  async connect(): Promise<boolean> {
    try {
      this.transport = new StdioClientTransport(this.serverParams);
      
      this.client = new Client(
        {
          name: "mcp-typescript-client",
          version: "1.0.0"
        },
        {
          capabilities: {
            prompts: {},
            resources: {},
            tools: {}
          }
        }
      );

      await this.client.connect(this.transport);
      console.log("Conexión exitosa con servidor MCP");
      return true;
    } catch (e) {
      console.error(`Error al conectar con servidor MCP: ${e}`);
      await this.disconnect();
      return false;
    }
  }

  async listTools(): Promise<any> {
    if (!this.client) {
      throw new Error("Cliente no conectado. Llama a connect() primero");
    }
    return await this.client.listTools();
  }

  async executeTool(toolName: string, args: Record<string, any>): Promise<any> {
    if (!this.client) {
      throw new Error("Cliente no conectado. Llama a connect() primero");
    }
    
    const result = await this.client.callTool({
      name: toolName,
      arguments: args
    });
    
    return result;
  }

  async disconnect(): Promise<void> {
    if (this.client) {
      await this.client.close();
      this.client = null;
    }
    this.transport = null;
  }
}

​
Ollama API Client

Create a client to communicate with Ollama’s API:
src/ollamaClient.ts
import fetch from 'node-fetch';

interface MessageType {
  role: string;
  content: string | null;
  tool_calls?: any[];
}

export class OllamaAPIClient {
  private baseUrl: string;

  constructor(baseUrl: string = "http://localhost:11434") {
    this.baseUrl = baseUrl;
  }

  async checkConnection(): Promise<boolean> {
    const response = await fetch(`${this.baseUrl}/api/tags`);
    if (response.status !== 200) {
      throw new Error(`Error al conectarse: ${response.status}`);
    }
    return true;
  }

  async chat(
    model: string,
    messages: MessageType[],
    tools?: any[],
    options?: any
  ): Promise<string | { type: string; function_call: any }> {
    const data: any = {
      model: model,
      messages: messages,
      stream: false
    };

    if (tools) {
      data.tools = tools;
    }

    const response = await fetch(
      `${this.baseUrl}/api/chat`,
      {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify(data),
      }
    );

    const responseText = await response.text();
    return this._processResponse(responseText);
  }

  private _processResponse(responseText: string) {
    const lines = responseText.trim().split('\n');
    let fullResponse = "";

    for (const line of lines) {
      const respJson = JSON.parse(line);

      // Check for function call
      if (respJson.message?.tool_calls) {
        const functionCall = respJson.message.tool_calls[0];
        if (functionCall) {
          return {
            type: "function_call",
            function_call: functionCall
          };
        }
      }

      // Accumulate normal response
      if (respJson.message?.content) {
        fullResponse += respJson.message.content;
      }
    }

    return fullResponse;
  }
}

​
Integrating MCP Tools with Ollama

The key is converting MCP tools to Ollama’s function calling format:
src/ollamaAgent.ts
class ToolManager {
  getAllTools(mcpTools: any = null): any[] {
    const tools = [];

    // Convert MCP tools to Ollama format
    if (mcpTools?.tools) {
      for (const mcpTool of mcpTools.tools) {
        tools.push({
          type: 'function',
          function: {
            name: `mcp_${mcpTool.name}`,
            description: mcpTool.description || `MCP tool: ${mcpTool.name}`,
            parameters: mcpTool.inputSchema || { type: 'object' }
          }
        });
      }
    }

    return tools;
  }
}

class OllamaAgent {
  private ollamaClient: OllamaAPIClient;
  private mcpClient: MCPClient;
  private toolManager: ToolManager;
  private toolsMCP: any = null;

  constructor(
    ollamaUrl: string = "http://localhost:11434",
    mcpCommand: string = "node",
    mcpArgs: string[] = ["path/to/server.js"]
  ) {
    this.ollamaClient = new OllamaAPIClient(ollamaUrl);
    this.mcpClient = new MCPClient(mcpCommand, mcpArgs);
    this.toolManager = new ToolManager();
  }

  async setup(): Promise<void> {
    // Verify Ollama connection
    await this.ollamaClient.checkConnection();
    
    // Connect to MCP server
    const connected = await this.mcpClient.connect();
    if (connected) {
      this.toolsMCP = await this.mcpClient.listTools();
    }
  }

  async executeMcpTool(toolName: string, args: Record<string, any>) {
    return await this.mcpClient.executeTool(toolName, args);
  }

  async chat(model: string, messages: any[], options?: any) {
    const tools = this.toolManager.getAllTools(this.toolsMCP);
    return this.ollamaClient.chat(model, messages, tools, options);
  }
}

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Function Execution

Handle function calls from Ollama: 
async function executeFunction(
  functionName: string,
  functionArgs: Record<string, any>,
  agent: OllamaAgent
): Promise<string> {
  // Check if it's an MCP tool (prefixed with "mcp_")
  if (functionName.startsWith("mcp_")) {
    const actualToolName = functionName.substring(4);
    const result = await agent.executeMcpTool(actualToolName, functionArgs);
    return JSON.stringify(result);
  }

  return `Function ${functionName} not implemented`;
}

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Python Implementation

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Installation

Create a pyproject.toml file:
pyproject.toml
[project]
name = "ollama-mcp-client"
version = "0.1.0"
requires-python = ">=3.13"
dependencies = [
    "mcp[cli]>=1.6.0",
    "requests>=2.32.3",
]
Install dependencies: 
uv pip install -e .

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MCP Client

mcp_client.py
import logging
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
from typing import Optional, Dict, Any

logger = logging.getLogger(__name__)

class MCPClient:
    def __init__(self, command: str, args: list[str], env: Optional[Dict[str, str]] = None):
        self.server_params = StdioServerParameters(
            command=command,
            args=args,
            env=env
        )
        self.session = None

    async def connect(self) -> bool:
        try:
            self._client_ctx = stdio_client(self.server_params)
            client = await self._client_ctx.__aenter__()
            self.read, self.write = client
            self._session_ctx = ClientSession(self.read, self.write)
            self.session = await self._session_ctx.__aenter__()
            await self.session.initialize()
            logger.info("Connected to MCP server")
            return True
        except Exception as e:
            logger.error(f"Error connecting to MCP server: {e}")
            return False

    async def list_tools(self) -> Any:
        if not self.session:
            raise RuntimeError("Not connected. Call connect() first")
        return await self.session.list_tools()

    async def execute_tool(self, tool_name: str, arguments: Dict[str, Any]) -> Any:
        if not self.session:
            raise RuntimeError("Not connected. Call connect() first")
        return await self.session.call_tool(tool_name, arguments)

​
Ollama API Client

ollama_client.py
import requests
import json
from typing import List, Dict, Any, Union

class OllamaAPIClient:
    def __init__(self, base_url: str = "http://localhost:11434"):
        self.base_url = base_url

    def check_connection(self) -> bool:
        response = requests.get(f"{self.base_url}/api/tags")
        if response.status_code != 200:
            raise Exception(f"Error connecting: {response.status_code}")
        return True

    def chat(
        self, 
        model: str, 
        messages: List[Dict[str, Any]], 
        tools: List[Dict[str, Any]] = None,
        options: Dict[str, Any] = None
    ) -> Union[str, Dict[str, Any]]:
        data = {
            "model": model,
            "messages": messages,
            "stream": False
        }
        
        if tools:
            data["tools"] = tools

        response = requests.post(
            f"{self.base_url}/api/chat", 
            json=data, 
            timeout=60
        )
        
        return self._process_response(response.text)

    def _process_response(self, response_text: str):
        lines = response_text.strip().split('\n')
        full_response = ""
        
        for line in lines:
            resp_json = json.loads(line)
            
            # Check for function call
            if "message" in resp_json and "tool_calls" in resp_json["message"]:
                function_call = resp_json["message"]["tool_calls"][0]
                if function_call:
                    return {
                        "type": "function_call",
                        "function_call": function_call
                    }
            
            # Accumulate normal response
            if "message" in resp_json and "content" in resp_json["message"]:
                content = resp_json["message"].get("content")
                if content:
                    full_response += content
        
        return full_response

​
Python Agent

agent.py
class OllamaAgent:
    def __init__(
        self, 
        ollama_url: str = "http://localhost:11434",
        mcp_command: str = "node",
        mcp_args: List[str] = None
    ):
        self.ollama_client = OllamaAPIClient(ollama_url)
        self.mcp_client = MCPClient(mcp_command, mcp_args or [])
        self.toolsMCP = None

    async def setup(self):
        # Verify Ollama connection
        self.ollama_client.check_connection()
        
        # Connect to MCP server
        await self.mcp_client.__aenter__()
        self.toolsMCP = await self.mcp_client.list_tools()

    def get_all_tools(self):
        tools = []
        
        if self.toolsMCP and hasattr(self.toolsMCP, 'tools'):
            for mcp_tool in self.toolsMCP.tools:
                tools.append({
                    'type': 'function',
                    'function': {
                        'name': f"mcp_{mcp_tool.name}",
                        'description': getattr(mcp_tool, 'description', f"MCP tool: {mcp_tool.name}"),
                        'parameters': getattr(mcp_tool, 'inputSchema', {'type': 'object'})
                    }
                })
        
        return tools

    def chat(self, model: str, messages: List[Dict], options: Dict = None):
        tools = self.get_all_tools()
        return self.ollama_client.chat(model, messages, tools, options)

​
Usage Example

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Interactive Chat

async function interactiveChat(agent: OllamaAgent) {
  const modelName = "mistral:latest";
  const messages = [
    {
      role: "system",
      content: "You are an agent with access to tools"
    }
  ];

  while (true) {
    const userMessage = await getUserInput();
    messages.push({ role: "user", content: userMessage });

    const response = await agent.chat(modelName, messages);

    if (typeof response === 'object' && response.type === "function_call") {
      // Execute the function
      const functionName = response.function_call.function.name;
      const functionArgs = JSON.parse(response.function_call.function.arguments);
      
      const result = await executeFunction(functionName, functionArgs, agent);
      
      // Add result to messages and get final response
      messages.push({
        role: "assistant",
        content: null,
        tool_calls: [response.function_call]
      });
      messages.push({
        role: "tool",
        content: result
      });
      
      const finalResponse = await agent.chat(modelName, messages);
      console.log(finalResponse);
    } else {
      console.log(response);
    }
  }
}

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Running the Application

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TypeScript

# Compile
npm run build

# Run
node dist/ollamaApp.js

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Python

python ollama-python-app.py

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Key Concepts

  1. Tool Conversion: MCP tools are prefixed with mcp_ and converted to Ollama’s function format
  2. Bidirectional Communication: The agent handles both normal chat and function calls
  3. Recursive Processing: Function calls can trigger additional function calls
  4. State Management: Messages history maintains context across tool executions

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Troubleshooting

  • Ensure Ollama is running: ollama serve
  • Check that your model supports function calling (e.g., mistral, llama3.1)
  • Verify MCP server path is correct
  • Check that all dependencies are installed

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Next Steps

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