Your AI tools are only as useful as the data they can reach. MCP servers are the infrastructure layer that connects AI agents to live enterprise data. They don’t require custom integration work for every system, switching platforms, or logging in to pull a report.
Before MCP, connecting an AI model to a business system required creating custom integration code for each combination. 4 AI applications x 8 business systems = 32 separate integration surfaces to build and maintain. Every model change could mean new integration work.
MCP collapses that into a hub-and-spoke model: one MCP server per system, one MCP client per AI application, and every combination can work through the same protocol. The common analogy is USB-C for AI: one universal adapter instead of a different connector for every device. Anthropic launched MCP in November 2024 with prebuilt servers for GitHub, Slack, Google Drive, Git, Postgres, and Puppeteer. By late 2025, Anthropic reported more than 10,000 active public MCP servers and 97M+ monthly SDK downloads across Python and TypeScript.
An MCP server makes three types of capabilities available to an agent: tools it can call to take actions, resources it can read to get data, and prompts that provide reusable templates. The AI model reads the server’s descriptions at runtime and decides what to use based on what the user asks. No more hardcoded logic or manual report pulling.
Practical example: a CIO asks their AI assistant how much the marketing team spent on AI tokens last month. The agent connects to the relevant MCP server, retrieves the data, and responds inside the AI tool where the question was asked. No dashboard switching required.
As task-specific agents move into enterprise applications, the integration question gets more strategic. It’s not enough for a vendor to have an API. Enterprise buyers need to know whether agents can access the right data through the right controls inside the tools employees already use.
Vendors are already moving in this direction. Atlassian’s Remote MCP Server brings Jira and Confluence data into external AI tools, Salesforce MCP solutions connect AI applications to Salesforce, Heroku, and MuleSoft. Microsoft has added MCP support in Copilot Studio and Microsoft 365 Copilot declarative agents.
For enterprise leaders, three things shift.
In our work with clients, the organizations moving fastest on MCP treat it as infrastructure, not a feature. They build the server layer once and make it available to every AI tool in their environment rather than rebuilding integrations for each new model they evaluate.
Larridin’s MCP server gives any MCP-compatible AI agent direct access to your organization’s AI measurement and optimization data: utilization by team, token spend by workflow, proficiency signals, and business value attribution. A leader using Claude, ChatGPT, or Copilot can ask a natural language question and get a live answer without opening larridin.com, running a report, or asking someone else to pull the data.
The Larridin MCP server is read-only for reporting queries, with access controls scoped to authorized users and data types.
Book a Discovery Call to see how the Larridin MCP server connects to your AI environment.
An MCP server is a lightweight process that exposes data, tools, or actions to AI agents using the Model Context Protocol. It describes its capabilities in a way the AI model can read at runtime, so the agent can discover and use those capabilities without hardcoded integration logic. One MCP server built for a business system works with every MCP-compatible AI model.
The Model Context Protocol is an open standard released by Anthropic in November 2024. It defines how AI agents discover, connect to, and interact with external tools and data sources. It has since been adopted across major AI products, including Claude, ChatGPT, Gemini, Microsoft Copilot, Visual Studio Code, and others, making it a shared foundation for AI-to-system integration.
No. Once an MCP server is connected to your AI client, you access the data it makes available through natural language inside the tool you already use. You don’t need to switch platforms, open a dashboard, or run a manual report. The agent retrieves the data and brings it directly into your conversation.
A regular API requires hardcoded logic: a developer specifies which endpoint to call and what parameters to pass. AI agents are non-deterministic: they read server descriptions and decide what to use based on a natural language request. MCP adds a discovery layer and session handling that standard API calls don’t provide. APIs are for programs; MCP is for AI agents.
Want to query your AI measurement data directly from the tools your team already uses?
Book a Discovery Call to see how the Larridin MCP server connects to your environment.