ai-llm-agentic-tooling-mcp

# AI LLM Agentic Tooling with Model Context Protocol (MCP) Implements best practices and guidelines for applying the Model Context Protocol (MCP) in AI and LLM environments. Focuses on the effective management of servers, clients, tools, resources, and prompts. ## Use Cases Use this skill when: - Building scalable LLM applications that require contextual awareness. - Managing resources in a multi-layered AI architecture. - Implementing protocols for efficient handling of contexts and state. ## Implementation Patterns This skill outlines the implementation of best practices for applying the Model Context Protocol (MCP) in AI and LLM environments. It focuses on efficient management of resources and contextual state to optimize performance and ease-of-use for users and developers alike. ### Basic MCP Implementation Here's how to initiate a basic Model Context: ```python class ModelContext: def __init__(self): self.context = {} def update_context(self, key: str, value: str): self.context[key] = value def get_context(self, key: str) -> str: return self.context.get(key, "") ``` ### Advanced Context Management This section includes management strategies for context handling: ```python class AdvancedModelContext(ModelContext): def merge_context(self, new_context: dict): self.context.update(new_context) def clear_context(self): self.context.clear() ``` ### Constraints on Use Ensure adherence to the following constraints when working with MCP: - Maintain strict input/output structures for context objects. - Implement thorough logging to track context changes. ## Metadata Updates ```yaml archetypes: tactical anti_triggers: - generic model context - vague context request response_profile: verbosity: medium directive_strength: high abstraction_level: operational ``` ### Basic MCP Implementation ```python class ModelContext: def __init__(self): self.context = {} def update_context(self, key: str, value: str): self.context[key] = value def get_context(self, key: str) -> str: return self.context.get(key, "") ``` ### Advanced Context Management ```python class AdvancedModelContext(ModelContext): def merge_context(self, new_context: dict): self.context.update(new_context) def clear_context(self): self.context.clear() ``` ### Constraints on Use - Ensure prompt structures are maintained to maximize performance and clarity. - Validate all context objects to ensure they adhere to expected formats and types.