What is Model Context Protocol (MCP)?

The short version

• MCP is an open standard (originally from Anthropic) for connecting AI assistants to tools and data.
• MCP servers expose three primitives: tools, resources, and prompts.
• It matters because it replaces per-vendor integration work with a single protocol that any MCP-aware AI client can consume.

The longer explanation

The problem MCP solves

Before MCP, if you wanted an AI assistant to work with your Slack workspace, your Google Drive, your internal database, and your ticketing system, you wrote four custom integrations — and then you wrote them again for every AI vendor you used. The integration work dominated the engineering spend. Worse, the security reviews multiplied: each AI vendor's access to each internal system was a separate decision, a separate audit, and a separate maintenance burden.

MCP collapses that combinatorial problem. An MCP server exposes a standard interface; any MCP-aware AI client (Claude, increasingly GPT and others, plus open-source agent frameworks) can consume it. Write the Slack server once, use it with any model.

The three primitives

• Tools. Functions the LLM can call, with typed arguments and returns. A Slack MCP server might expose post_message(channel, text), search_messages(query), and get_user_profile(user_id).
• Resources. Data the LLM can read. A Google Drive server might expose the contents of a specific folder as readable resources the model can retrieve on demand.
• Prompts. Parameterized prompt templates a server can offer, which a client can use or pass to the model. Useful for servers that want to suggest "here is how I should be used".

Where MCP fits in enterprise architecture

Two deployment patterns are common:

Gateway pattern: An internal team stands up an MCP gateway that aggregates the client's approved servers. Internal AI agents and users connect to the gateway; the gateway handles authentication, audit, and policy enforcement. This is the right pattern for security-sensitive environments.

Direct pattern: An AI application talks to MCP servers directly (local servers for filesystem access, hosted servers for SaaS integrations). Simpler, appropriate for individual developer tooling and low-risk use cases.

Enterprise adoption considerations

• Auth and authorization. MCP leaves auth to the server implementation. Enterprises standardize on OAuth, SAML, or service accounts depending on the target system. Per-user scoping is the norm for human-initiated agents.
• Audit. Every tool call and resource read should be logged. Most production MCP deployments include a trace layer on top of the protocol.
• Security review. An MCP server effectively grants the AI whatever the server's credentials grant. Treat MCP server permissions as carefully as you would treat a service account.
• Versioning. Tool signatures evolve; a new argument to a tool can break agents that expected the old shape. Versioning discipline on the server side is important.

How Thoughtwave approaches this

Our TWSS CS Agent uses MCP as its retrieval and tool layer — product knowledge, regulatory sources, and the historical case store are all MCP providers. Our TWSS AI Custom Agents platform adopts MCP as the canonical tool protocol for every agent on the platform. Clients get the portability benefit (same tool catalog across models and agents) plus the governance benefit (centralized audit and policy enforcement).

For broader context, see our AI & Generative AI service and the accelerators portfolio.