Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open standard introduced by Anthropic that provides a universal interface for connecting AI models to external data sources, tools, and services. MCP defines a client-server architecture where AI applications (clients) communicate with data providers (servers) through a standardized protocol, eliminating the need for custom integrations per data source.
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What Is Model Context Protocol (MCP)?
Before MCP, connecting an LLM to external tools and data required building custom integrations for every combination of AI application and data source. If you wanted your AI assistant to access your company's database, CRM, file storage, and code repository, you needed four separate integrations, each with its own authentication, data formatting, and error handling logic. MCP solves this by defining a universal protocol: build one MCP server for each data source, and any MCP-compatible AI client can connect to it immediately.
MCP follows a client-server architecture with three core components: hosts (AI applications like Claude Desktop or IDE assistants), clients (protocol connectors that maintain server connections), and servers (lightweight programs that expose data and tools through the standard protocol). Servers expose three types of capabilities: resources (read-only data like files and database records), tools (executable functions like running queries or sending emails), and prompts (templated interaction patterns). This standardized interface means a single MCP server for PostgreSQL works with every MCP-compatible AI client.
The protocol supports two transport mechanisms: stdio (for local servers running as child processes) and HTTP with Server-Sent Events (for remote servers accessible over the network). Local transport is simpler and more secure for desktop tools. Remote transport enables cloud deployments and shared server infrastructure. MCP also handles capability negotiation, allowing clients and servers to agree on supported features during initialization.
MCP is gaining rapid adoption across the AI ecosystem. Anthropic's Claude Desktop, Cursor IDE, and several other AI tools support MCP natively. The open-source community has built MCP servers for databases, APIs, file systems, and developer tools. Salt Technologies AI expects MCP to become the standard integration layer for enterprise AI deployments, replacing custom REST API wrappers and ad-hoc tool integrations within the next 12 to 18 months.
Real-World Use Cases
Enterprise Data Integration
A company builds MCP servers for their Salesforce CRM, PostgreSQL database, and Confluence wiki. Their AI assistant can now query customer records, pull analytics, and search documentation through a unified interface, without building three separate integrations.
Developer Tooling
A development team connects their IDE-based AI assistant to GitHub, Jira, and their internal deployment system via MCP servers. The assistant can create pull requests, update tickets, and trigger deployments through natural language commands, all through standardized MCP connections.
Multi-Source Research Assistant
A consulting firm deploys MCP servers for financial data APIs, news feeds, and internal knowledge bases. Their research assistant queries all sources through MCP, producing comprehensive analysis reports without analysts needing to manually aggregate data from multiple platforms.
Common Misconceptions
MCP is only for Anthropic/Claude products.
MCP is an open protocol that any AI application can implement. While Anthropic introduced it, the specification is open-source and framework-agnostic. Cursor IDE, various open-source tools, and a growing ecosystem of third-party clients already support MCP.
MCP replaces existing APIs and integrations.
MCP provides a standardized wrapper around existing APIs, it does not replace them. An MCP server for Salesforce still calls the Salesforce API internally. MCP standardizes how AI applications discover and interact with these APIs, reducing integration overhead.
MCP servers are complex to build.
Simple MCP servers can be built in under 100 lines of code using the official SDKs for Python and TypeScript. The protocol handles connection management, message formatting, and capability negotiation, so server authors focus only on their data access logic.
Why Model Context Protocol (MCP) Matters for Your Business
MCP eliminates the N-times-M integration problem in enterprise AI: without it, connecting N AI tools to M data sources requires N times M custom integrations. With MCP, you build M servers and N clients, and they all interoperate. This reduces integration effort by 60-80% and enables faster deployment of AI tools across an organization. For companies building AI-powered workflows that span multiple internal systems, MCP is becoming the standard integration layer.
How Salt Technologies AI Uses Model Context Protocol (MCP)
Salt Technologies AI builds custom MCP servers for client data sources as part of our AI Integration and AI Agent Development packages. We deploy MCP servers for databases, internal APIs, and SaaS tools, enabling client AI systems to access data through a standardized, secure interface. Our MCP implementations include authentication handling, rate limiting, and audit logging. We leverage the MCP ecosystem to accelerate integration timelines from weeks to days.
Further Reading
- AI Readiness Checklist 2026
Salt Technologies AI Blog
- AI Development Cost Benchmark 2026
Salt Technologies AI Datasets
- Model Context Protocol Specification
Anthropic
Related Terms
Function Calling / Tool Use
Function calling (also called tool use) is an LLM capability where the model generates structured requests to invoke external functions, APIs, or tools rather than producing only text responses. The model receives function definitions (name, parameters, descriptions), decides when a function is needed, and outputs a structured call that the application executes. This bridges the gap between language understanding and real-world actions.
AI Agent
An AI agent is an autonomous software system that uses LLMs to perceive its environment, make decisions, and take actions to accomplish goals with minimal human intervention. Unlike simple chatbots that respond to single queries, agents can plan multi-step workflows, use tools (APIs, databases, code execution), maintain memory across interactions, and adapt their strategy based on intermediate results.
Agentic Workflow
An agentic workflow is an AI architecture where a language model autonomously plans, executes, and iterates on multi-step tasks using tools, APIs, and reasoning loops. Unlike single-prompt interactions, agentic workflows break complex goals into subtasks, evaluate intermediate results, and adapt their approach dynamically. This pattern enables AI to handle real-world business processes that require judgment, branching logic, and external system interaction.
AI Orchestration
AI orchestration is the coordination layer that manages the execution flow of multi-step AI workflows, routing tasks between models, tools, databases, and human reviewers. It handles sequencing, parallelization, error recovery, state management, and resource allocation across AI pipeline components. Orchestration transforms individual AI capabilities into coherent, production-grade systems.
LangChain
LangChain is an open-source orchestration framework that simplifies building applications powered by large language models. It provides modular components for chaining prompts, retrieving context, calling tools, and managing memory across conversational and agentic workflows.
Anthropic Claude API
The Anthropic Claude API provides access to the Claude family of large language models, known for their strong instruction following, long-context handling (up to 200K tokens), and safety-focused design. Claude models are a leading alternative to OpenAI for enterprise AI applications that require thoughtful, nuanced responses.