The Model Context Protocol (MCP), released by Anthropic, is an open-standard protocol designed to solve the data integration bottleneck for AI models. While LLMs excel at reasoning, they operate in isolation from your local codebase, database tables, and external SaaS services. Historically, developers bridged this gap by writing custom scrapers, one-off API integrations, or copy-pasting text files. MCP replaces these ad-hoc solutions with a single, unified communication standard.

The Architecture: Client, Server, and Data Source

The protocol follows a clean Client-Server-Data Source hierarchy. Instead of connecting a model directly to a database or filesystem, the host application interfaces with modular servers that act as translators.

  • The Client (Host): The orchestration layer. This is typically your development tool (e.g., Cursor IDE, Zed, or Claude Desktop). The client manages user permissions, initiates connections, and determines which files or tools the LLM is permitted to access.
  • The Server: A lightweight process or HTTP endpoint that wraps a specific data source or utility. The server exposes data and actions using standard MCP JSON-RPC schemas.
  • The Data Source: The underlying asset—such as a PostgreSQL database, a local directory, or a third-party API (like Stripe or HubSpot).

The Three Core Primitives

MCP servers expose their capabilities to clients through three primary protocol primitives:

  1. Resources (Read-Only Context): Static text records, binary files, or data schemas. They are represented using custom URIs (e.g., db://postgres/users/schema). Resources allow your AI to read documentation or inspect databases without execution side-effects.
  2. Tools (Executable Actions): Functions that the model can request to execute (e.g., writing files, running terminal commands, or triggering API calls). Tool inputs are validated using standard JSON Schema.
  3. Prompts (Templates): Pre-defined templates and system instructions that guide the model's behavior for specific tasks (such as code reviews or bug inspections).

Under the Hood: JSON-RPC 2.0 and Transport Pipes

Communication between the client and server is conducted over JSON-RPC 2.0, a lightweight remote procedure call protocol. When you boot your IDE, it spawns the MCP server as a subprocess and initiates a version negotiation handshake:

// Client initialize request payload
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "initialize",
  "params": {
    "protocolVersion": "2024-11-05",
    "clientInfo": { "name": "Cursor-IDE", "version": "0.45.0" }
  }
}

Once initialized, the client can query the server's capabilities using tools/list or resources/list, and invoke tools via tools/call.

MCP supports two transport protocols depending on deployment needs:

  • Stdio Transport: Used for local processes. The client spawns the server as a child process and reads/writes messages via standard standard input and output (stdin/stdout).
  • Stderr Logging: The child process uses standard error (stderr) for logs, which the client reads but ignores as protocol content.
  • SSE (Server-Sent Events) Transport: Used for remote network servers. The server streams events to the client over HTTP, and the client sends requests back upstream via standard HTTP POST calls.

Connecting Servers to Cursor and Claude

Connecting an MCP server to your local development environment requires updating a configuration file. Below are the standard connection formats:

Claude Desktop Configuration

Configure servers in your claude_desktop_config.json file:

{
  "mcpServers": {
    "local-filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/username/Documents"
      ]
    }
  }
}

Cursor IDE Configuration

For project-specific access, configure a local .cursor/mcp.json file at the root of your repository:

{
  "mcpServers": {
    "local-terminal": {
      "command": "uvx",
      "args": ["mcp-server-terminal"]
    }
  }
}

Where ContextCove Fits in the MCP Ecosystem

ContextCove operates as a specialized documentation MCP server. Instead of copy-pasting API references or manually uploading documentation Markdown files into Claude Projects or Cursor rules, ContextCove exposes your curated local documentation packs as read-only MCP resources.

Your LLM can query the ContextCove MCP server to search and read fresh, boilerplate-free documentation chunks in real time, ensuring it writes code aligned with current library specifications without bloating your prompt token window.