Agent Skills and the Model Context Protocol (MCP) are complementary, not redundant. Skills package internal, repeatable workflows for 'doing the thing,' while MCP provides the open standard for connecting to external systems like databases and APIs for 'reaching the thing.'

Traditional API integration requires strict adherence to a predefined contract. The new AI paradigm flips this: developers can describe their desired data format in a manifest file, and the AI handles the translation, dramatically lowering integration barriers and complexity.

To avoid overwhelming an LLM's context with hundreds of tools, a dynamic MCP approach offers just three: one to list available API endpoints, one to get details on a specific endpoint, and one to execute it. This scales well but increases latency and complexity due to the multiple turns required for a single action.

MCP shouldn't be thought of as just another developer API like REST. Its true purpose is to enable seamless, consumer-focused pluggability. In a successful future, a user's mom wouldn't know what MCP is; her AI application would just connect to the right services automatically to get tasks done.

Exposing your platform via a Model Consumable Platform (MCP) does more than enable integrations. It acts as a research tool. By observing where developers and LLMs succeed or fail when calling your API, you can discover emergent use cases and find inspiration for new, polished AI-native product features.

The technical term "MCP" (Model Component Provider) is confusing. It's simpler and more accurate to think of them as connectors that give AI tools access to knowledge within your other apps and the ability to perform actions in them.

To make an AI data analyst reliable, create a 'Master Claude Prompt' (MCP) with 3 example queries demonstrating key tables, joins, and analytical patterns. This provides guardrails so the AI consistently accesses data correctly and avoids starting from scratch with each request, improving reliability for all users.

Tasklet's experience shows AI agents can be more effective directly calling HTTP APIs using scraped documentation than using the specialized MCP framework. This "direct API" approach is so reliable that users prefer it over official MCP integrations, challenging the assumption that structured protocols are superior.

Exposing a full API via the Model Context Protocol (MCP) overwhelms an LLM's context window and reasoning. This forces developers to abandon exposing their entire service and instead manually craft a few highly specific tools, limiting the AI's capabilities and defeating the "do anything" vision of agents.