Open-source large language models (LLMs) like Llama that can be run locally are increasingly attractive for organizations seeking greater control, privacy, and performance from their generative AI deployments. But without access to timely enterprise data, LLMs struggle to deliver meaningful, context-aware outputs, as their reasoning capabilities are only as strong as the information available.
CData Model Context Protocol (MCP) Servers help bridge this gap, enabling seamless access to live, governed data from virtually any system directly into the context window of an LLM, whether deployed locally or in the cloud. In this post, we discuss why local LLMs are gaining popularity and how MCP empowers live, secure access to enterprise data sources.
Why local LLMs are gaining ground
LLMs are increasingly being adopted in organizations, especially when it comes to generating functional code, enhancing automation, and drafting messages. And while cloud-hosted models offer convenience, they can often come with unforeseen trade-offs such as a lack of control over model behavior, varying usage costs, and risking data privacy. In contrast, open-source models such as Meta’s Llama family have made it feasible to run powerful LLMs on local hardware.
Developer’s laptops or on-premises GPU clusters can now run these models locally, which allows enterprises to maintain full control over the model’s behavior, avoid vendor lock-in, and enforce strict data privacy and compliance policies. Open-source models give full access to model weights and architecture, so developers can fine-tune and customize their on-premises models to align with the specific needs and wants of their organization. Because of this, local LLMs are best suited to assist with embedding intelligence in workflows, driving automation, and unlocking competitive advantages.
While local LLMs offer exceptional benefits, they can face a key limitation of being isolated from real-time systems and platforms that hold operational data needed for effective and intelligent interactions with the model. Because they are hosted locally, the knowledge of the model can become static and stuck at the point in time when the model was last trained or fine-tuned. This isolation can restrict the model's usefulness for many enterprise use cases where data accuracy and recency are key. However, there is an answer to this: MCP.
Introducing Model Context Protocol
MCP is an open-source standard for enabling AI systems, particularly LLMs, to interact securely with business tools and enterprise data sources. It defines a consistent protocol for systems to share relevant information with an AI model the moment it needs it. This transforms the static local models into dynamic, context-aware systems that can actively engage with live enterprise data to make data-driven decisions and outputs.
MCP provides a structured and standardized way for enterprise systems to make their data and functions accessible to LLMs. It helps facilitate a pipeline that:
Interprets the given prompt/instruction to determine the required data.
Executes parameterized queries against enterprise data sources.
Transforms results into structured context objects for model ingestion.
By handling everything from accessing data to processing query results, MCP empowers LLMs and AI tools to use the freshest possible data, which in turn minimizes latency, preserves data security, and eliminates the need for preloading information or data.
Connecting Llama to enterprise data
To illustrate MCP in action, let's walk through an example of using Meta’s Llama 3.1 model locally with LM Studio, integrated with the CData MCP Server for Salesforce.
Prerequisites
Step 1: Configure the MCP Server
To use the CData MCP Server, you will add the MCP Server to LM Studio by editing the app's mcp.json file:
In the Tools & Integrations section, click Install > Edit mcp.json. This will open the mcp.json file in the in-app editor.
Edit the file and add the CData MCP Server, then save your changes.
Image 1: Adding the CData MCP Server to the mcp.json file in LM Studio
Step 2: Download the model
In LM Studio, you can download an LLM directly on your computer. To do this, go to the Discover tab, then pick one of the curated options or search for the model you want to use:
Image 2: Downloading the LLM in LM Studio
Step 3: Chat with your data!
After configuring the CData MCP Server and downloading the Llama model, you can now create a new chat and talk to your live data. To do this, go to the Chat tab and open the model loader. Select the model you downloaded and load the model. Once the model is loaded, you can start a back-and-forth conversation with the model and your enterprise data.
Image 3: Using the MCP Server in LM Studio
Use cases powered by live context
Pairing the CData MCP Servers with AI tools opens a wide range of possibilities for logical applications. The examples below showcase how MCP Servers make it possible for local LLM interactions to utilize embedded enterprise intelligence:
AI copilots: Enhance internal copilots and AI agents with up-to-date CRM or ERP data to assist sales and customer support teams interacting with customers.
Data-aware chatbots: Implement AI chatbots that provide intelligent responses because it is backed with governed data that is pulled from back-end systems.
Analyst workflows: Equip analysts with secure and live operational insights without the need for manual querying and data processing.
Executive briefings: Generate on-demand reports and summaries that reference real-time KPIs from connected enterprise systems.
Customer service automation: Provide support and sales reps with live ticketing and account history to improve first-response accuracy.
Why it matters for IT and product leaders
Enterprise leaders are facing immense pressure to develop and implement effective AI processes that are both impactful and aligned with security and compliance requirements. Traditional approaches to this challenge can often lead to data duplication, engineering overhead, and complex integration work. With MCP for local LLMs, IT and product leaders can significantly reduce the complexity of these AI processes by providing secure, real-time access to enterprise data sources without the need to build new data pipelines.
This approach can accelerate time-to-value and speed innovation for AI initiatives by enabling secure, real-time access to enterprise data. Instead of building custom data pipelines or developing complex integrations, MCP allows organizations to use their own LLMs and plug into live data seamlessly to deliver intelligent and up-to-date experiences.
Bring your own model, not your Empower local LLMs with enterprise-grade data access. CData MCP Servers make it possible to deliver smarter, more context-aware AI experiences—without replicating your data or compromising control. Download the free beta and start exploring what your agents can do with real-time, governed data streams built for the age of enterprise AI.
Try CData MCP Servers Beta
As AI moves toward more contextual intelligence, CData MCP Servers can bridge the gap between your AI and business data.
