Model Context Protocol (MCP)

Enable intelligent context management across AI models and services.

Overview

Model Context Protocol (MCP) is a standardized protocol for managing and sharing context between AI models, services, and applications. It's the backbone of Skeet's AI capabilities, enabling coherent multi-turn interactions and context-aware responses.

MCP Overview

The architecture diagram above shows how The skeet mcp server connects to IDEs and MCP other MCP hosts.

Introduction to the Model Context Protocol (MCP)

Overview

The Model Context Protocol (MCP) is an open standard designed to standardize how applications provide context to Large Language Models (LLMs). Analogous to how USB-C offers a universal connection for various devices, MCP provides a standardized method for connecting AI models to diverse data sources and tools.

Purpose of MCP

MCP facilitates the development of agents and complex workflows atop LLMs by:

  • Offering a growing list of pre-built integrations that LLMs can directly utilize.
  • Providing flexibility to switch between different LLM providers and vendors.
  • Implementing best practices for securing data within an organization's infrastructure.

Architecture

MCP operates on a client-server architecture comprising:

  • MCP Hosts: Applications such as Claude Desktop, Integrated Development Environments (IDEs), or AI tools that access data through MCP.
  • MCP Clients: Protocol clients that maintain one-to-one connections with servers.
  • MCP Servers: Lightweight programs exposing specific capabilities via the standardized Model Context Protocol.
  • Local Data Sources: Files, databases, and services on a local machine that MCP servers can securely access.
  • Remote Services: External systems accessible over the internet (e.g., through APIs) that MCP servers can connect to.

Key Features

  • Standardization: MCP provides a universal protocol for connecting AI systems with data sources, replacing fragmented integrations with a single protocol.
  • Security: Emphasizes best practices for securing data within an organization's infrastructure.
  • Flexibility: Allows for seamless switching between different LLM providers and vendors.

How does Skeet MCP work?

MCP Architecture

The Skeet MCP architecture consists of several critical components working cohesively to facilitate smooth and secure context management:

  • MCP Hosts & Clients: IDEs, desktop applications, or developer tools initiate interactions with the MCP infrastructure.
  • Skeet MCP Server: This hosted external MCP server manages complex authentication processes, OAuth flows, and handles low-level networking tasks such as configuring load balancers and establishing secure Server-Sent Events (SSE) connections.
  • Integration Layer: Seamlessly connects with external services like Jira, Notion, Slack, GitHub, Linear, and more, managing permissions and scope intricacies so developers don't have to manually handle API keys or individual service authentications.
  • Context-Enriched API Calls: Unlike typical MCP implementations where clients must handle multiple, complex API interactions, Skeet MCP servers simplify workflows by making single, optimized API calls. These calls aggregate all necessary context automatically, preventing workflow interruptions or complexities related to fetching UUIDs, multiple API requests, or handling incomplete responses.

Problems Skeet MCP Solves

  • Authentication Complexity: Handles OAuth flows for multiple services, ensuring secure authentication without exposing sensitive API keys.
  • Networking Overhead: Manages underlying networking setups including load balancing and SSE, significantly reducing developer overhead.
  • Integration Hassles: Simplifies the integration with popular developer-focused tools (e.g., GitHub, Jira, Slack) by managing permission scopes and API keys seamlessly.
  • Contextual Complexity: Enhances standard MCP functionality by making single, context-rich API requests that encapsulate all necessary parameters, significantly reducing complexity and potential points of failure.
  • Developer Workflow Optimization: Specifically tailored integrations and workflows that enhance developer productivity rather than generic or overly complex configurations.

What's Next?

Now that you understand MCP, explore our integrations to see how it powers various development workflows:

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