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zeekay
0ebffbd529
- Fix 381 files where branding script corrupted variable names (e.g. isHanzo KMSTemplate -> isKmsTemplate) - Rebrand from "Hanzo KMS" to just "KMS" for white-label support (allows Lux KMS, Hanzo KMS, etc. via configuration) - 856 files, purely string replacements, no logic changes
53 lines
2.9 KiB
Plaintext
53 lines
2.9 KiB
Plaintext
---
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title: "Model Context Protocol"
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sidebarTitle: "What is MCP?"
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description: "Learn what the Model Context Protocol is and how KMS helps you manage it securely."
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---
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## What is the Model Context Protocol?
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The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) is an open standard that enables AI assistants and agents to securely connect to external data sources and tools. It provides a unified way for AI systems to interact with services like Notion, GitHub, Slack, databases, and more.
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Think of MCP as a universal adapter between AI systems and the tools they need. Instead of building custom integrations for each AI platform and each tool, MCP provides a standardized protocol that any AI client can use to connect to any MCP-compatible server.
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## How Does MCP Work?
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MCP follows a client-server architecture:
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- **MCP Clients**: AI applications like Claude, ChatGPT, or custom AI agents that need to access external tools.
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- **MCP Servers**: Services that expose tools and capabilities through the MCP protocol. These can be first-party servers (like Notion's official MCP server) or custom servers you build.
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When an AI assistant needs to perform an action—like searching Notion, creating a GitHub issue, or querying a database—it communicates with the appropriate MCP server using the standardized protocol.
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## Why Do You Need Agent Sentinel?
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While MCP provides the protocol for AI-tool communication, it doesn't solve the governance and security challenges that organizations face:
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### The Challenge
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- **Credential Sprawl**: Each user connecting AI tools to MCP servers manages their own credentials, creating security blind spots.
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- **No Visibility**: Organizations have no insight into what tools AI agents are using or what data they're accessing.
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- **Ungoverned Access**: Without centralized control, AI agents may access tools and data they shouldn't.
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- **Compliance Risk**: Audit requirements demand logging and monitoring of AI system activities.
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### The Solution
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KMS Agent Sentinel acts as a secure gateway between AI clients and MCP servers:
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```mermaid
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graph LR
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A[AI Client] --> B[KMS MCP Endpoint]
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B --> C[Notion]
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B --> D[GitHub]
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B --> E[Slack]
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```
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This architecture provides:
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1. **Centralized Credential Management**: Store and manage MCP server credentials in one place. Choose between **shared credentials** (one credential for all users) or **personal credentials** (each user authenticates individually) based on your security and compliance needs.
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2. **Tool Governance**: Control exactly which tools from each MCP server are available through your endpoints.
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3. **Complete Audit Trail**: Every tool invocation is logged with full request/response details, user attribution, and timestamps.
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4. **Access Control**: Leverage KMS's existing access control mechanisms to manage who can use which endpoints.
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Learn more about [credential modes](/documentation/platform/agent-sentinel/mcp-servers#credential-modes) when configuring MCP servers.
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