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On the Inevitability of Distributed AI Skills: The Transition from Monolithic Intelligence to a "Cloud-Based Brain"

#ai #mcp #java #solon

In the evolution of Artificial Intelligence from "Conversational Models" to "Agentic AI," we are at a critical turning point—similar to the transition of the Internet from standalone software to distributed architectures. At the heart of this transformation lies the rise of AI Skills and the inevitability of their distributed development.

1. What are AI Skills: Evolution from Tool-level to Framework-level

The concept of AI Skills was initially popularized in cutting-edge Agent practices like Claude Code. Originally, Skills were viewed as "tool-level" enhancements—simple file I/O or terminal operations—allowing users to quickly perform various tasks.

However, in modern application development frameworks like Solon AI, AI Skills have evolved into a higher-dimensional abstraction designed for Agent application development.

  • Tool-level: Solves the problem of the "hands"; these are specific execution functions.
  • Framework-level: Solves the problem of the "brain." A Skill is an aggregation of Tools, Instructions, and Metadata. It encompasses not just execution logic, but also admission control, instruction augmentation, and tool "tinting" (tracing/tagging) capabilities.

2. Core Characteristics of AI Skills

To address the issues of context noise, permission vacuums, and loss of behavioral control found in traditional Tool models, a mature AI Skill must possess the following traits:

  1. Intelligent Admission (isSupported): A Skill is activated only when specific intents, tenants, or environmental conditions (Prompt Context) are met. This prevents ineffective tools from cluttering the model's context and wasting tokens.
  2. Instruction Injection (getInstruction): Provides the model with "rules of conduct" based on the current context, solving the problem of how the model should act.
  3. Tool Routing (getTools): Dynamically distributes tools based on the current context.
  4. High Autonomy: Logic within a specific domain is handled as a closed loop within the Skill, outputting standardized results to the external environment.

3. MCP: The World Wide Web Protocol for the AI Era

With the explosion of skill demands, the Model Context Protocol (MCP) emerged. It is the standard protocol connecting AI models with external data and tools.

MCP is to AI what HTTP is to the World Wide Web.

In the Internet era, HTTP allows any browser to access resources on any server. In the AI era, MCP allows any Agent to seamlessly call skills provided by different vendors across different physical locations. This standardization completely shatters the "hard-coded" shackles between Agents and the external world.

4. Distributed Evolution of Tools: The Birth of MCP Tools

The form of "Tools" is undergoing a fundamental change: evolving from local monoliths to MCP Tools (Distributed Tools). These possess physical location transparency; they are no longer functions in local memory, but independent distributed capability nodes. This "node-ization" of capabilities is the first step for AI toward a microservices architecture.

  • Traditional Tools: Code-level coupling, running inside the Agent process, difficult to reuse across languages or environments.
  • MCP Tools (Distributed Tools): Exposed via the MCP protocol, offering location transparency. They function as independent capability nodes.

5. Architectural Mapping: From Distributed MCP Tools to MCP Skills

The distribution of Tools provides a natural path for the distribution of AI Skills. When we publish a set of Skills—comprising business logic, instruction guidance, and toolsets—via the MCP protocol, they evolve into MCP Skills.

We can clearly map the distributed blueprint of AI Agents to traditional architectures:

  • MCP is equivalent to RPC (Remote Procedure Call): It defines how models and capability nodes communicate; it is the underlying pipeline of the Agent world.
  • MCP Skills are equivalent to Microservices: Each Skill is an independent business unit with its own business semantics.

Note: Distributed AI Skills can also be implemented via traditional RPC systems, though this requires significantly more manual work.

6. Implementing MCP Skills: Collaboration between Client and Server

The core of implementing MCP Skills lies in mapping the lifecycle semantics of a Skill onto the endpoints of the MCP protocol.

1. McpSkillClient (Local Proxy for Remote Skills)

The McpSkillClient acts as a local proxy. Its responsibility is to shake hands with remote services and wrap network calls into a Skill interface.

  • Metadata Perception: Synchronizes remote metadata via agreed-upon paths.
  • Dynamic Mapping: At runtime, it converts local isSupported or getInstruction calls into remote MCP Tool calls.
  • Tool Filtering: Automatically removes management tools marked as hide, presenting the LLM only with the business tools relevant at that moment.

Application Example:

// 1. Build the MCP client provider (handles protocol communication and schema caching)
McpClientProvider mcpClient = McpClientProvider.builder()
                .channel(McpChannel.STREAMABLE)
                .url("http://localhost:8081/skill/order")
                .build();

// 2. Evolve the MCP client into a Skill proxy
McpSkillClient skillClient = new McpSkillClient(mcpClient);

// 3. Build a Prompt with business context
Prompt prompt = Prompt.of("Order: A001, please query order details.")
                .attrPut("tenant_id", "1")       // Inject tenant context
                .attrPut("user_role", "admin");  // Inject role permissions

// 4. Call the LLM; the skill will automatically complete: remote admission, instruction retrieval, and tool filtering
chatModel.prompt(prompt)
          .options(o -> o.skillAdd(skillClient))
          .call();
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2. McpSkillServer (Perceptive Skill Server)

By extending McpSkillServer, developers can export local business logic as remote skills.

  • Lifecycle Exposure: Uses @ToolMapping and @ResourceMapping to export logic like isSupported and getInstruction.
  • Intelligent Perception: The server perceives intent through the passed Prompt state. For example, it decides which tools to return via getToolsName based on user roles.
  • Security Tagging: Adds hide:1 tags to management endpoints to ensure system-level instructions are not leaked to the model.

Application Example:

@McpServerEndpoint(channel = McpChannel.STREAMABLE_STATELESS, mcpEndpoint = "/skill/order")
public class OrderManagerSkillServer extends McpSkillServer {

    @Override
    public String description() {
        return "Expert skill for order queries and cancellations";
    }

    // Intelligent Admission: Decide whether to respond based on Prompt content and attributes
    @Override
    public boolean isSupported(Prompt prompt) {
        // Semantic check: Is the intent relevant?
        boolean isOrderTask = prompt.getUserContent().contains("order");
        // Security check: Tenant ID must be present
        boolean hasTenant = prompt.attr("tenant_id") != null;

        return isOrderTask && hasTenant;
    }

    // Dynamic Instruction: Inject real-time "rules of conduct" for the LLM based on context
    @Override
    public String getInstruction(Prompt prompt) {
        String tenantName = prompt.attrOrDefault("tenant_name", "Unknown Tenant");
        return "You are now the order supervisor for [" + tenantName + "]. Process only order data for this tenant; cross-tenant queries are forbidden.";
    }

    // Attachment Hook: Triggered when the skill is activated; used for logs or initializing context
    @Override
    public void onAttach(Prompt prompt) {
        // Few-shot or background knowledge can be injected here via prompt.addMessage()
        System.out.println("Order skill attached. Current tenant: " + prompt.attr("tenant_id"));
    }

    /**
     * Dynamic Capability Discovery: Decide which tools to expose based on user permissions
     * @return null: expose all; Empty: disable all; List: precisely expose specific tools.
     */
    @Override
    public List<String> getToolsName(Prompt prompt) {
        List<String> tools = new ArrayList<>();

        // Basic permission: Visible to all compliant users
        tools.add("OrderQueryTool");

        // Fine-grained permission: Cancellation tool visible only to ADMIN role
        if ("ADMIN".equals(prompt.attr("user_role"))) {
            tools.add("OrderCancelTool");
        }

        return tools;
    }

    @ToolMapping(description = "Query details by order ID")
    public String OrderQueryTool(String orderId) {
        return "Order " + orderId + " Status: Shipped";
    }

    @ToolMapping(description = "Cancel specific order")
    public String OrderCancelTool(String orderId) {
        return "Order " + orderId + " successfully cancelled";
    }
}
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7. Summary of Inevitability

The distributed path for AI Skills is irreversible:

  • Decoupling and Reuse: Complex skills (e.g., legal auditing, professional code refactoring) no longer need to be rewritten in every project; they exist independently as services.
  • Security Boundaries: Sensitive data processing skills can be deployed in private, protected intranets, communicating with public Agents only through controlled MCP protocols.
  • Heterogeneous Ecosystems: Capabilities across different languages and computing environments can connect via a unified MCP interface, forming a true "Agent Microservices Network."

The emergence of MCP Skills (currently an architectural pattern rather than a formal specification) marks the end of the "personal toolbox" era in AI application development and the beginning of a true "Agent Microservices Network" era.

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