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What is MCP Integration?

The Model Context Protocol (MCP) integration is a lightweight approach to adding human approval workflows to your AI applications and agents. It’s specifically designed for use with Large Language Models (LLMs) like GPT, Claude, and others, and integrates seamlessly with popular AI frameworks.
MCP integration is ideal for AI tools and frameworks where simplicity is important and you want minimal changes to your existing code.

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Benefits of MCP Integration

Minimal Code Changes

Integrate with just a few lines of code in your existing LLM applications.

Framework Agnostic

Works with OpenAI, Anthropic, LangChain, AutoGen, and more.

Zero Context Overhead

No need to add approval-specific instructions to your LLM prompts.

Automatic Tool Validation

Functions requiring approval are automatically validated before execution.

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Getting Started

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Install the SDK

npm install @okgotcha/sdk

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Create Your Tool Functions

Define your tool functions as normal, then wrap the ones that require approval: 
import { OkGotcha } from '@okgotcha/sdk';
import OpenAI from 'openai';

// Initialize OkGotcha
const ok = OkGotcha();

// Define your tools
const tools = {
  // Regular function - no approval needed
  getAccountBalance: async (accountId: string): Promise<number> => {
    // Implementation here
    return 10000; // Example
  },
  
  // Function requiring approval
  transferFunds: ok.requireApproval({
    title: "Fund Transfer",
    description: "Transfer funds between accounts",
    approvers: ["finance-team", "security-team"]
  })(async (fromAccount: string, toAccount: string, amount: number): Promise<boolean> => {
    // Implementation here
    return true;
  })
};

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Define OpenAI Tool Schema

Define the tool schema as you normally would for OpenAI: 
// Define OpenAI tools schema
const openaiTools = [
  {
    type: "function",
    function: {
      name: "getAccountBalance",
      description: "Get the current balance of an account",
      parameters: {
        type: "object",
        properties: {
          accountId: { type: "string" }
        },
        required: ["accountId"]
      }
    }
  },
  {
    type: "function",
    function: {
      name: "transferFunds",
      description: "Transfer funds between accounts",
      parameters: {
        type: "object",
        properties: {
          fromAccount: { type: "string" },
          toAccount: { type: "string" },
          amount: { type: "number" }
        },
        required: ["fromAccount", "toAccount", "amount"]
      }
    }
  }
];

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Use with OpenAI

import OpenAI from 'openai';

// Create OpenAI client
const openai = new OpenAI();

// Example of using tools with OpenAI
async function processUserRequest(userInput: string) {
  const messages = [{ role: "user", content: userInput }];
  
  let response = await openai.chat.completions.create({
    model: "gpt-3.5-turbo-1106",
    messages,
    tools: openaiTools,
    tool_choice: "auto"
  });

  while (response.choices[0].message.tool_calls) {
    messages.push(response.choices[0].message);
    
    // Handle tool calls
    for (const toolCall of response.choices[0].message.tool_calls) {
      const toolName = toolCall.function.name;
      const toolArgs = JSON.parse(toolCall.function.arguments);
      
      try {
        // This will automatically handle approval if needed
        const toolResult = await tools[toolName](...Object.values(toolArgs));
        
        messages.push({
          role: "tool",
          name: toolName,
          content: JSON.stringify(toolResult),
          tool_call_id: toolCall.id
        });
      } catch (error) {
        if (error instanceof OkGotcha.PendingApprovalError) {
          messages.push({
            role: "tool",
            name: toolName,
            content: JSON.stringify({
              status: "pending_approval",
              message: "This action requires human approval. Waiting for approval...",
              approvalId: error.approvalId
            }),
            tool_call_id: toolCall.id
          });
        } else {
          messages.push({
            role: "tool",
            name: toolName,
            content: JSON.stringify({ error: error.message }),
            tool_call_id: toolCall.id
          });
        }
      }
    }

    // Continue the conversation
    response = await openai.chat.completions.create({
      model: "gpt-3.5-turbo-1106",
      messages,
      tools: openaiTools
    });
  }

  return response.choices[0].message.content;
}

// Use the tools
const result = await processUserRequest(
  "Transfer $5000 from account 123 to account 456"
);

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Framework Integrations

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LangChain Integration

import { OkGotcha } from '@okgotcha/sdk';
import { ChatOpenAI } from "langchain/chat_models/openai";
import { 
  ChatPromptTemplate, 
  MessagesPlaceholder 
} from "langchain/prompts";
import { createOpenAIFunctionsAgent } from "langchain/agents";

const ok = OkGotcha();

// Create your tools with approval requirements
const tools = [
  new DynamicTool({
    name: "get_account_balance",
    description: "Get the current balance of an account",
    func: async (accountId: string) => {
      return "10000";
    },
  }),
  new DynamicTool({
    name: "transfer_funds",
    description: "Transfer funds between accounts",
    func: ok.requireApproval({
      title: "Fund Transfer",
      description: "Transfer funds between accounts",
      approvers: ["finance-team"]
    })(async (input: string) => {
      const { fromAccount, toAccount, amount } = JSON.parse(input);
      // Transfer implementation
      return "Transfer initiated successfully";
    }),
  }),
];

// Create the agent
const chat = new ChatOpenAI();
const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful banking assistant."],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
  new MessagesPlaceholder("agent_scratchpad"),
]);

const agent = await createOpenAIFunctionsAgent({
  llm: chat,
  tools,
  prompt,
});

const agentExecutor = AgentExecutor.fromAgentAndTools({
  agent,
  tools,
});

// Execute the agent
const result = await agentExecutor.invoke({
  input: "Transfer $5000 from account 123 to account 456",
  chat_history: [],
});

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Approval Management

When using MCP integration, approvals are managed the same way as with the SDK integration:
  1. Approvers receive notifications through configured channels
  2. Approvers can approve or reject requests through the OK!Gotcha dashboard
  3. Upon approval, the function executes and results are returned to the agent
  4. If rejected, an error is returned to the agent
You can also check approval status programmatically: 
const status = await ok.getApprovalStatus(approvalId);

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Next Steps