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model context protocol

The Model Context Protocol (MCP) emerged as a response to the increasing complexity of interactions with large language models (LLMs). Early models like GPT-2 and GPT-3 used a simple “stateless” prompt-based approach, meaning each query had to include all relevant context. This limited persistent memory and made sophisticated applications difficult to build.

By 2023, OpenAI, Anthropic, and others began exploring context windows—the maximum amount of information a model could "remember" in a single session. With GPT-4 and Claude, context lengths increased dramatically (up to 200K tokens), enabling models to process entire documents, codebases, or long conversations at once.

Still, developers needed more structured, reliable ways to manage context. This led to the emergence of Model Context Protocols: conventions or APIs that structure interactions, persist important state across sessions, and enable modular memory (e.g., long-term vs. short-term). Protocols like LangChain, Semantic Kernel, and OpenAI’s function calling and tool use features became foundational.

Modern MCPs support agent frameworks, retrieval-augmented generation (RAG), fine-grained memory, and dynamic tool usage. By 2025, MCPs have become essential for AI-first systems, transforming LLMs from static responders into persistent, context-aware collaborators.

Purpose of This Standardized Interface

By following these steps, developers can greatly expand AI agent capabilities, allowing it to perform more sophisticated and context-aware tasks by integrating with a wide range of external resources and functionalities.

The Model Context Protocol (MCP) is an open standard that allows AI systems, especially large language models (LLMs) like Gemini, to integrate and share data with external tools, systems, and data sources. Think of it as a standardized interface that lets AI agents:

Purpose of the Model Context Protocol

The main goal of MCP is to let AI models reach beyond their inherent knowledge and interact with the real world and specific environments. This helps solve several key challenges:

  1. Breaking Information Silos: While LLMs are powerful, their knowledge is usually limited to their training data. MCP lets them access current, specialized, or private information from various outside sources (e.g., a company's internal documents, live web data, or local file systems).
  2. Enabling Tool Use: AI models can become "agents" that perform complex tasks by using a set of tools. MCP offers a standard way for these tools (exposed as "MCP servers") to tell the AI client what they can do, and for the AI to call these tools with the needed context.
  3. Enhancing Contextual Understanding: For tasks like code analysis, problem-solving, or content generation, the AI needs to deeply understand the specific context (e.g., an entire codebase, a project's history, or user-specific data). MCP allows relevant context to be given to the model in a structured and efficient way, even for large amounts of data.
  4. Promoting Interoperability: As an open standard, MCP allows different AI clients (like Claude Desktop or Gemini CLI) to connect with various MCP servers, creating a rich ecosystem of AI-powered tools and integrations. This means developers aren't stuck with just one vendor.
  5. Facilitating Agentic Workflows: MCP is essential for building multi-tool AI agents that can link multiple actions and reason across distributed resources to achieve complex goals.

MCP lets the CLI integrate with your local system and external services, making Gemini a more powerful and adaptable assistant directly from your terminal. For example, it can use Gemini's large context window for deep analysis of big files or codebases by bringing those files into the model's context through an MCP server.

Defining Clear Context Boundaries for a Model Context Protocol Configuration

Defining clear context boundaries in a Model Context Protocol (MCP) configuration is critical to ensuring a machine learning model operates reliably, is maintainable, and integrates seamlessly into production systems.

The MCP serves as a structured framework to encapsulate the contextual information a model needs—such as input data, feature specifications, environment variables, and metadata—while avoiding ambiguity or unintended dependencies.

Below, I’ll outline a detailed, step-by-step approach to defining clear context boundaries when composing an MCP configuration, grounded in best practices from real-world software engineering.

Step 1: Identify and Scope the Model’s Context Requirements

The first step is to thoroughly understand what contextual information the model requires to function correctly.

This involves collaboration with data scientists, domain experts, and stakeholders to capture all necessary inputs and dependencies.

Pro Tip: Use a requirements traceability matrix to map model requirements to specific context elements, ensuring nothing is overlooked.

Step 2: Create a Formal Schema for the Context

To enforce clear boundaries, define a formal schema for the MCP configuration using a structured, machine-readable format. This schema acts as a contract that specifies the structure, types, and constraints of the context.


	{
	"type": "object",
	"properties": {
	"customer_age": {
	"type": "integer",
	"minimum": 18,
	"maximum": 120,
	"required": true
	},
	"purchase_history": {
	"type": "array",
	"items": {
	"type": "object",
	"properties": {
	"date": { "type": "string", "format": "date" },
	"amount": { "type": "number", "minimum": 0 }
	}
	}
	}
	},
	"required": ["customer_age"]
	}

Pro Tip: Use schema validation libraries like jsonschema (Python) or avro (Java) to automatically validate context data against the schema during runtime.

Step 3: Modularize the Context Configuration

To maintain clarity and scalability, modularize the MCP configuration into logical components. This prevents the configuration from becoming a monolithic, unmanageable file and makes it easier to update specific parts without affecting others.


	    model_context:
	    features:
	    customer:
	    age: { type: integer, min: 18 }
	    last_purchase: { type: string, format: date }
	    environment:
	    api_key: { type: string, required: true }
	    gpu_enabled: { type: boolean, default: false }
	

	    feature_mapping:
	    $ref: "file://feature_mapping.yaml"

Pro Tip: Store modular configuration files in a version-controlled repository (e.g., Git) to track changes and enable rollbacks.

Step 4: Enforce Strict Input and Output Boundaries

Define clear boundaries for what enters and exits the model’s context to prevent unintended side effects or data leakage.


	    output_schema:
	    prediction: { type: string, enum: ["positive", "negative"] }
	    confidence: { type: number, minimum: 0, maximum: 1 }

Pro Tip: Use tools like OpenAPI or GraphQL schemas to define and enforce input/output contracts for API-based model deployments.

Step 5: Document and Communicate the Configuration

Clear documentation is essential to ensure all team members understand the context boundaries and can work with the MCP effectively.


	    customer_age:
	    type: integer
	    description: "Age of the customer in years, used for churn prediction"
	    min: 18
	    max: 120

Pro Tip: Regularly review the documentation with your team to ensure it stays up-to-date with changes to the model or its context.

Step 6: Test and Validate the Context Boundaries

Testing the MCP configuration ensures that the defined boundaries are robust and effective.

Pro Tip: Use property-based testing (e.g., with Python’s hypothesis library) to automatically generate a wide range of valid and invalid context inputs to stress-test the boundaries.

Step 7: Monitor and Audit Context Usage

Once the MCP is in production, continuously monitor and audit how the context is used to ensure the boundaries remain intact.

Pro Tip: Implement a feedback loop where production issues inform updates to the MCP configuration, tightening or adjusting boundaries as needed.

Best Practices and Lessons Learned

Example MCP Configuration

Here’s a simplified example of an MCP configuration in YAML, demonstrating clear context boundaries:


	    model_context_protocol:
	    version: "1.0.0"
	    description: "Context configuration for customer churn prediction model"
	    schema:
	    features:
	    customer_age:
	    type: integer
	    description: "Customer's age in years"
	    required: true
	    min: 18
	    max: 120
	    purchase_history:
	    type: array
	    description: "List of purchase records"
	    items:
	    date: { type: string, format: date }
	    amount: { type: number, min: 0 }
	    last_login_date:
	    type: string
	    format: date
	    required: false
	    environment:
	    model_version: { type: string, default: "v1.0" }
	    inference_mode: { type: string, enum: ["batch", "realtime"], default: "realtime" }
	    output:
	    prediction: { type: string, enum: ["churn", "stay"] }
	    confidence: { type: number, min: 0, max: 1 }

This configuration clearly defines the input features, environmental settings, and output expectations, with explicit constraints to enforce boundaries.

How a Developer Sets It

To set up and use MCP with the Gemini CLI, developers typically follow these steps:

Choose or Develop an MCP Server:

Existing MCP Servers: Many community-made MCP servers exist for various purposes (e.g., file system access, web Browse, specific API integrations like GitHub, or database interaction). You can choose and install one that fits your needs.

Custom MCP Server: If a pre-built server doesn't exist for your specific use case, you can develop your own. MCP servers are usually light programs that expose specific abilities through the standard Model Context Protocol. They define tools with schemas and implement the functionality.

Configure the Gemini CLI to use the MCP Server:

The Gemini CLI's settings, typically in ~/.gemini/settings.json, are used to configure MCP servers. You'll need to add the MCP server's configuration to this JSON file. This usually involves specifying the command to run the server and any arguments.

Example of settings.json configuration (conceptual):

{
	    "mcpServers": {
	    "gemini-cli-file-server": {
		"command": "npx", // Or "python", "npm", etc.
		"args": ["-y", "gemini-mcp-tool"] // Or "path/to/your/server.py", "server-name"
		},
		"my-custom-api-server": {
		"command": "python",
		"args": ["/path/to/my_api_mcp_server.py"],
		"env": {
		"API_KEY": "your_secret_api_key"
		}
		}
		}
		}

After changing the settings.json file, you might need to reload the Gemini CLI or the associated client (e.g., in VS Code, "Developer: Reload Window" if using Gemini Code Assist).

Utilize in Prompts:

Once configured, the Gemini CLI (or an integrated client like Gemini Code Assist) will know about the tools and context provided by the MCP servers. You can then use these tools within your prompts. For example, you might use @syntax to refer to files for analysis, or call specific commands exposed by the MCP server.

For example: /analyze prompt:@src/ summarize this directory

The Gemini CLI offers commands like /mcp to list configured MCP servers and their available tools, /tools to display all available tools, and /sandbox to test code safely within an isolated environment.


Key Conventions in Model Context Protocols

1. Function Calling / Tool Use

What it is:
Function calling allows a language model to invoke external functions, tools, or APIs by emitting structured JSON. The model decides when and how to use a tool based on the input or dialogue.

Why it’s useful:

{
	"function": "get_weather",
	"parameters": { "location": "Chicago" }
	}

2. Assistant-Style Chat Protocols

What it is:
A structured conversation format using message roles like user, assistant, and system. Each message is a step in the interaction.

Why it’s useful:

[
	{ "role": "user", "content": "Tell me a joke." },
	{ "role": "assistant", "content": "Why don't scientists trust atoms? Because they make up everything!" }
	]

3. System Messages and Role-Based Formatting

What it is:
System messages provide behavioral instructions to guide the model. Roles clarify the intent and source of each message.

Why it’s useful:


	{ "role": "system", "content": "You are a helpful technical assistant who answers concisely." }

4. Threaded Memory APIs (e.g., OpenAI Assistants)

What it is: A persistent memory system where each thread contains ongoing dialogue, file attachments, tool calls, and metadata. Used to maintain state across sessions.

Why it’s useful:

Example: Thread ID abc123 stores:

last updated: 2025-07-16

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