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Overview

Model Context Protocol (MCP) is an open standard that enables AI models to seamlessly discover and execute external tools at runtime. Instead of being limited to text generation, AI models can interact with filesystems, search the web, query databases, and execute custom business logic through external MCP servers.

DeepIntShield provides a comprehensive MCP integration that goes beyond simple tool execution:

  • MCP Client: Connect to any MCP-compatible server (filesystem tools, web search, databases, etc.)
  • MCP Server: Expose your connected tools to external MCP clients (like Claude Desktop)
  • Agent Mode: Autonomous tool execution with configurable auto-approval
  • Code Mode: Let AI write and execute Python to orchestrate multiple tools

Key Security Principles:

PrincipleDescription
Explicit ExecutionTool calls from LLMs are suggestions only - execution requires separate API call
Granular ControlFilter tools per-request, per-client, or per-virtual-key
Opt-in Auto-executionAgent mode with auto-execution must be explicitly configured
Stateless DesignEach API call is independent - your app controls conversation state

Connect to MCP Servers

Connect to external MCP servers via STDIO, HTTP, or SSE protocols with automatic retry logic

Open →

OAuth Authentication

Secure OAuth 2.0 authentication with automatic token refresh

Open →

Tool Execution

Execute tools with full control over approval and conversation flow

Open →

Agent Mode

Enable autonomous tool execution with configurable auto-approval

Open →

Code Mode

Let AI write Python to orchestrate multiple tools in one request

Open →

Connection Resilience

Transient failures recover on their own and disconnected servers reconnect automatically

Open →

MCP Gateway URL

Expose DeepIntShield as an MCP server for Claude Desktop and other clients

Open →

Tool Filtering

Control which tools are available per request or per virtual key

Open →

DeepIntShield works as an MCP client (you connect it to external tool servers) and, in the Gateway deployment, as an MCP server (you expose your aggregated tools to external clients like Claude Desktop).

By default DeepIntShield does not execute tool calls automatically - you stay in control. The workflow you implement is:

  1. POST /v1/chat/completions - the model returns tool-call suggestions (not executed).
  2. Review the tool calls - apply your security rules or ask the user to approve.
  3. POST /v1/mcp/tool/execute - execute the approved tool calls.
  4. POST /v1/chat/completions - continue the conversation with the tool results.

This gives you explicit approval gates, no accidental writes/deletes to external services, and a full audit trail. See Tool Execution for the end-to-end code. To let approved tools run automatically instead, enable Agent Mode.

If you connect 3+ MCP servers, turn on Code Mode. It keeps the model’s context compact and typically cuts token cost by ~50% and latency by 40-50% on multi-step workflows, because the model writes one short script instead of round-tripping through every tool definition. Enable it per client - see Code Mode.


Set up your first MCP client connection →

Learn about header-based and OAuth 2.0 authentication →

Learn how Code Mode reduces costs by 50% →

Learn the tool execution workflow →

Configure autonomous tool execution →