Model-Context Protocol (MCP) is a standardized layer that allows an LLM to communicate with various software tools without needing custom integrations for each. It acts like a universal translator, enabling the LLM to 'speak English' while the MCP handles communication with each tool's unique API.

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.

Enterprises are currently overspending on tokens by sending all queries to the most powerful LLMs. A new software category will emerge to intelligently route requests to smaller, cheaper models when possible, creating a critical efficiency and cost-saving layer between companies and foundational model providers.

Words like "feature" mean different things to a GIS system versus GitHub. A virtual MCP server (a proxy layer) can create curated, semantically unambiguous toolsets for specific agents or tasks, preventing model confusion and improving reliability.

To overcome LLM limitations, successful Model Context Protocol (MCP) design involves severe constraints: keep the number of tools low, use precise yet concise names and descriptions, minimize input parameters, and return only essential data. This handcrafted approach is necessary for models to perform reliably.

To bypass context window limits with large APIs, Stainless uses a 'dynamic mode' for its MCP servers. It provides only three tools: `list endpoints`, `get endpoint details`, and `execute endpoint`. This scales infinitely but adds latency, as the model needs three separate turns to perform a single action.

The growth of LLM context windows has stalled not primarily due to technical barriers, but because multi-million token requests can cost users several dollars per query, leading to low demand. The industry is shifting focus to "smart context" techniques like compaction and retrieval to provide relevant information without the prohibitive cost of massive context.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

To solve the problem of MCPs consuming excessive context, advanced AI clients like Cursor are implementing "dynamic tool calling." This uses a RAG-like approach to search for and load only the most relevant tools for a given user query, rather than pre-loading the entire available toolset.