AI agents and Large Language Models (LLMs) offer broad abilities, but their true power emerges when they can access specific external data or perform tasks in a highly consistent manner. Achieving this requires careful context engineering, which involves providing the model with the precise information it needs to generate accurate and relevant outputs. Two primary methods for extending LLM what it does in this way are the Model Context Protocol (MCP) and the use of dedicated skills.
The Foundation: Context Engineering for LLMs
At its core, an LLM functions as a sophisticated prediction machine, trained on vast amounts of information like books, magazines, and internet content. This training allows it to recognize patterns and answer a wide array of general questions. However, for an AI agent to perform specialized tasks, such as inspecting a specific database or updating customer information, the LLM needs more than just general knowledge. It requires specific context.
Prompt engineering involves giving the LLM a task with a specific role. Context engineering goes further by adding all necessary additional information. This might include details about how data should be formatted, how a particular database is configured, or even the results from a tool that has already fetched data. Providing the right context helps the LLM make the correct decision and generate the desired answer. The challenge lies in efficiently and reliably supplying this context to build effective AI agents.
Model Context Protocol (MCP): Connecting to External Data
The Model Context Protocol (MCP) provides a standardized way for an AI model to interact with external data sources. Imagine an AI agent needing to access data from a customer relationship manager (CRM) or other service APIs. Instead of manually copying and pasting API documentation, tokens, and data into the LLM’s context window, MCP streamlines this process.
MCP works by abstracting service APIs into a format that an LLM can readily understand and use. It also handles authentication, which is vital for secure data access. This includes providing uniquely scoped tokens and managing read-write permissions as needed. Behind the scenes, an MCP server integrates with the AI application or development environment. When the LLM needs information from an external service, it generates a specific JSON request. The MCP server then translates this JSON request into standard web calls, such as POST or GET requests, to interact with the actual service.
MCP acts as a standardized layer between the LLM and the necessary data sources. It is widely supported by almost all AI tools and is open source. This approach is particularly useful for situations where an AI application needs real-time data access in a controlled and tightly permissioned way. Examples include querying the status of virtual machines, checking the state of a cluster, or retrieving specific customer records from a CRM.
Skills: Embedding Domain Knowledge and Repeatable Actions
While MCP excels at connecting to real-time external data, there is another aspect to extending LLMs: providing domain knowledge and ensuring repeatable actions. LLMs are known for their non-deterministic nature, meaning they might not always produce the exact same output for the same input, even when given similar context. This can be problematic when a specific task, like formatting sales data, needs to be done the exact same way every time.
This is where skills become essential. A skill is a packaged unit that contains specific prompts, scripts, and resources, designed to teach an LLM how to perform a task consistently. Think of tasks like cleaning up Excel documents, debugging code, or running compliance checks. The prompts and scripts used for these tasks can be bundled into a skill.
A skill is typically structured as a markdown file within a folder, containing metadata. This metadata includes the skill’s title, a clear description of when it should be used, and the actual prompt that will be passed to the LLM. A key feature of skills is their ability to be auto-loaded into the LLM’s context window only when needed. For example, a code debugger skill would only be loaded when the agent is asked about code errors. Skills can also include additional resources and scripts within their folder, providing more context or abilities that are loaded alongside the main prompt.
Skills work across all major AI tools and models and are open source. They are lightweight and effective for adding reusable, custom abilities to an AI agent, ensuring consistent output for specific, repetitive tasks, such as formatting customer contact information to include their name, contact details, and even their favorite type of cookie.
Choosing Between MCP and Skills
The decision to use Model Context Protocol (MCP), skills, or a combination of both depends on the specific requirements of the AI agent and its workload. Both approaches enhance the context window of an LLM, helping it to generate more accurate and useful outputs.
MCP is the preferred choice when an AI application requires access to real-time, dynamic data from external sources. It acts as an integration layer, allowing agents to call specific tools and resources with controlled permissions. If the agent needs to know “what VMs are currently running?” or “what is the current cluster state?”, MCP provides the structured, secure pathway to retrieve this live information. However, setting up and configuring MCP can be quite involved, potentially becoming overkill for simpler, internal abilities.
Skills, on the other hand, are ideal for embedding specific domain knowledge or ensuring consistent, repeatable execution of tasks. They teach the model how to do something, rather than just fetching data. If the goal is to consistently format data, debug code according to specific guidelines, or analyze investment data using a predefined script, skills offer a lightweight and effective solution. They address the non-deterministic nature of LLMs by providing a structured, reusable set of instructions.
Both MCP and skills are open source and widely adopted in most AI tools today. They can be used locally, making them accessible for developers. Often, the most effective AI agents will use both. MCP can provide the real-time data, while skills can process and format that data according to specific, consistent business rules or domain expertise.
Optimizing AI Agent Performance
Optimizing AI agent performance hinges on providing the LLM with the most relevant and accurate context for its tasks. By strategically employing MCP and skills, developers can much enhance an agent’s what it does and reliability. MCP allows agents to interact dynamically with the outside world, pulling in fresh data as needed, which is critical for applications requiring up-to-the-minute information. This integration capability ensures that agents are not limited to their pre-trained knowledge but can operate on current realities.
Conversely, skills empower agents with specialized knowledge and the ability to execute complex, multi-step processes with consistent results. This is particularly valuable in environments where precision and adherence to specific protocols are paramount. By combining these two methods, an AI agent can be both data-aware and task-proficient, leading to more intelligent, adaptable, and performant solutions for a wide range of applications. The strategic choice between MCP and skills, or their combined use, directly impacts an agent’s ability to deliver precise and valuable outputs.
