MCP Server & AI Agent Communications

Overview: What is MCP?

The Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024. It provides a universal way for AI assistants to connect with external data sources, tools, and services — replacing one-off integrations with a single, standardised protocol.

Think of it like USB-C for AI — instead of building a custom connector for every data source (databases, APIs, file systems, etc.), you build once against the MCP standard and everything works.

MCP is now at 87,000+ GitHub stars across its reference servers and has SDKs in 10 languages (Python, TypeScript, Go, Rust, Java, Kotlin, C#, PHP, Ruby, Swift).

MCP Architecture

Core Components

• MCP Host — The AI application (Claude Desktop, Hermes Agent, IDE plugins) that needs access to data and tools
• MCP Client — A 1:1 connection from the host to a server. Manages session lifecycle, capability negotiation, and request/response flow
• MCP Server — Exposes resources, tools, and prompts. Can be local (stdio) or remote (HTTP)

Transport Types

• Stdio Transport — Server runs as a subprocess, communicates over stdin/stdout. Best for local, low-latency access
• HTTP / StreamableHTTP — Server runs remotely. Used for shared/enterprise deployments, enables auth, rate limiting

MCP in AI Agent Communications

MCP enables a standardised agent-to-tool communication pattern that solves the fragmentation problem in AI agent ecosystems. Here's how it works in practice:

1. Agent → Tool (Function Calling)

The AI agent (Host) uses MCP tools to interact with the outside world. The server advertises available tools via tools/list, and the agent calls them via tools/call. This replaces custom function-calling implementations with a standard protocol.

2. Agent → Data (Context Injection)

MCP resources expose data to the agent — files, database records, API responses. Unlike RAG pipelines that require custom embedding/retrieval setups, MCP provides a standard way for agents to pull in context from any source.

3. Server → Agent (Sampling / Reverse Flow)

MCP servers can request LLM completions from the agent host via sampling. This enables agent-in-the-loop workflows — the server says "I need the agent to reason about this data" and the host handles the LLM call. Hermes Agent supports this natively.

4. Multi-Agent Coordination

With multiple MCP servers connected, an agent can coordinate across:

• Filesystem server → read/write project files
• GitHub server → manage repos, PRs, issues
• Database server → query data
• Custom servers → your own business logic

All through one standard protocol — no custom connectors needed.

How Hermes Agent Uses MCP

Hermes Agent has a built-in native MCP client that auto-discovers and registers tools from MCP servers at startup. Here's the workflow:

Configured in ~/.hermes/config.yaml:

mcp_servers:
  time:
    command: "uvx"
    args: ["mcp-server-time"]
  
  filesystem:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"]
  
  github:
    command: "npx"
    args: ["-y", "@modelcontextprotocol/server-github"]
    env:
      GITHUB_PERSONAL_ACCESS_TOKEN: "ghp_..."

Key Benefits for AI Agents

• 🔌 Plug & Play — Add mcp_servers: to config, tools auto-appear. No code changes.
• 🔒 Security — Environment filtering (only safe vars passed to servers), credential stripping in error messages, per-server env config
• 🔄 Auto-Reconnect — Exponential backoff (up to 5 retries, 60s max) if a server drops
• 🧩 Multi-Server — Run any number of servers simultaneously, tools are namespaced (mcp_{server}_{tool})
• 🌐 Transport Flexibility — Local stdio for low latency, HTTP for remote/enterprise
• 🔄 Bidirectional — Sampling allows servers to request LLM completions from the agent
• 📦 87,000+ Reference Servers — Pre-built servers for GitHub, Filesystem, PostgreSQL, Time, Memory, Git, Google Drive, Slack, and more

Available Reference Servers

Official MCP reference implementations include: Everything Fetch Filesystem Git Memory Sequential Thinking Time

Community servers include: Brave Search, GitHub, GitLab, Google Drive, Google Maps, PostgreSQL, Puppeteer, Redis, Sentry, Slack, SQLite, and 5,000+ more on the MCP Registry.

Sources & Further Reading

• MCP Architecture Overview — Official docs
• Introducing MCP — Anthropic's announcement (Nov 2024)
• MCP Reference Servers — 87k ★ GitHub repo
• Awesome MCP Servers — Curated list of community servers
• MCP Specification — Full protocol spec