MCP (Model Context Protocol) is the standard way to give AI agents access to external tools and data. It works well. It is also a context window hog. Every MCP server you connect adds tool definitions, capability descriptions, and schema information to your context. Connect five MCP servers and you have burned 3,000-5,000 tokens before the conversation even starts. Apideck CLI takes a different approach that trades some flexibility for dramatically lower context consumption.
The Context Window Problem with MCP
When you connect an MCP server, the agent needs to know what tools are available, what parameters they accept, and what they return. This information gets injected into the system prompt or early conversation context. For a single MCP server with 5-10 tools, that is maybe 500-800 tokens. Manageable.
But real agent setups have multiple MCP servers. A file system server. A database server. A web search server. An email server. A calendar server. Each one adds its tool definitions to the context. At scale, the tool definitions alone can consume 20-30% of your available context window.
This is not a theoretical problem. I have hit it repeatedly. My agent setup with 8 MCP servers burned about 6,000 tokens on tool definitions alone. That is tokens that could be used for actual conversation, file content, or task context. In a 128K context window, 6K tokens is a rounding error. In a 16K window (which many fast, cheap models use), it is a significant chunk.
How Apideck CLI Differs
Apideck CLI does not expose a dynamic tool catalog. Instead, it provides a fixed, compact API surface for common integrations (CRM, email, calendar, file storage, accounting, HR, and about 20 other categories). Each category has a standardized set of operations (list, get, create, update, delete) with unified schemas.
The context cost of Apideck CLI is roughly 400 tokens total, regardless of how many integrations you enable. That is because the operations are standardized - the agent learns "list," "get," "create," "update," "delete" once, and those verbs apply uniformly across all integrations. Compare that to MCP where every server defines its own unique tool names, parameter schemas, and return types.
The CLI part is important. Apideck runs as a command-line tool that the agent invokes, not as a protocol server that maintains a connection. The agent calls apideck crm contacts list --limit 10 and gets JSON back. No handshake, no capability negotiation, no persistent connection.
The Tradeoffs Are Real
Apideck's standardized approach means you lose the flexibility to define custom tool behaviors. MCP lets you build arbitrarily complex tools - a tool that queries a database, processes the results, and writes a report, all in one invocation. Apideck gives you CRUD operations and that is mostly it. Complex workflows require multiple Apideck calls composed by the agent.
The integration breadth is also different. MCP is an open protocol - anyone can build an MCP server for anything. Apideck supports a curated set of integrations. If your tool is not in their catalog, you cannot use it through Apideck. You need a separate MCP server or a custom tool definition for that specific integration.
When to Use Which
My current setup uses both. Apideck CLI handles the standard integrations - CRM, email, calendar, file storage. These are high-frequency, low-complexity operations where the standardized CRUD interface is perfectly sufficient. The context savings from using Apideck for these instead of 5 separate MCP servers is about 4,000 tokens.
MCP handles the specialized tools - my custom database queries, code execution, web scraping, and domain-specific integrations that Apideck does not cover. These genuinely need the flexibility of custom tool definitions.
The Bigger Question
Apideck CLI raises a question that the MCP community should think about: do we need dynamic tool discovery for every integration? The MCP approach of "every server describes itself at connection time" is powerful but expensive. For standardized operations against common services, a fixed interface that the model already knows how to use is more efficient.
I suspect we will see more tools take the Apideck approach - standardized, low-context interfaces for common operations, with MCP reserved for truly custom tools. The context window is a finite resource, and we should be spending it on the conversation, not on telling the model that "gmail.send" takes a "to" field, a "subject" field, and a "body" field for the thousandth time.
Context efficiency is an underrated dimension of agent architecture. Apideck CLI is a practical reminder that less protocol overhead sometimes means more useful work.