MCP Agent Patterns

MCP Agent Patterns (Model Context Protocol)

Overview

The Model Context Protocol (MCP) provides a standardized way for AI agents to interact with external tools, data resources, and environments. This skill defines architectural patterns and implementation best practices for building, connecting, and optimizing MCP-based systems within the KILO-KIT ecosystem.

When To Use

• Implementing custom MCP servers or clients.
• Integrating external services (APIs, DBs, filesystems) as MCP servers.
• Optimizing context windows when dealing with large tool catalogs.
• Implementing real-time resource synchronization or complex multi-server orchestrations.
• Security-critical integrations requiring isolated execution environments.

Core MCP Concepts

• Tools: Executable functions exposed to the LLM.
• Resources: Data sources that can be read by the LLM (e.g., file contents, database records).
• Prompts: Reusable templates or workflows designed to be used by the LLM.
• Sampling: A protocol allowing an MCP server to request the host client to perform LLM inference on its behalf, enabling agentic tool behavior.

Advanced Patterns

Dynamic Tool Catalog & Progressive Loading

To prevent token bloat:

1. Manifest Exposure: Expose only essential, high-level tool definitions initially.
2. On-Demand Loading: Load detailed JSON Schemas only when a tool is selected or explicitly requested by the agent context.

MCP Sampling Protocol

When a server needs LLM intelligence (e.g., parsing unstructured output or making a decision):

• The server issues a sampling/createMessage request to the client.
• The client acts as the host/gateway, maintaining control over the model selection and cost.

Resource Streaming & Subscriptions

• Use URI Templates (res://server/type/{id}) to create flexible resource paths.
• Implement Resource Subscriptions to push data updates from the server to the client without polling.

Eager vs Lazy Tool Registration

• Eager: Best for small sets of essential core tools (zero-latency startup).
• Lazy: Mandatory for large or dynamic catalogs to keep initial prompts lean.

Multi-Server Orchestration

• Namespace Routing: Prefix tool names with server aliases (serverA:toolX).
• Conflict Resolution: Implement a central router to handle collisions between tools of the same name across different servers.

Security Considerations

• Server Sandboxing: Run MCP servers in isolated containers or limited-permission processes.
• Credential Isolation: Inject sensitive environment variables (API keys, tokens) only into the specific server process that requires them. Use a secure configuration store (e.g., HashiCorp Vault) for production deployments.

Quality Gates

• Contract Verification: Ensure MCP server implementations conform to the JSON-RPC message schema.
• Context Hygiene: Verify that only relevant tools/resources are included in the prompt; prune dormant subscriptions.
• Error Handling: Standardize error codes for common failures (e.g., timeout, auth denied, connection lost).

MCP 2.0 Features (2025-2026)

• OAuth 2.0 Auth: Native support for delegated access without sharing long-lived keys.
• Elicitation API: Interactive flow where the client helps the user provide missing inputs for a tool call.
• Structured Tool Results: Formalized response types beyond simple strings (e.g., rich content types, UI components).

References

• MCP Official Documentation
• JSON-RPC 2.0 Specification
• KILO-KIT MCP Integration Best Practices