SaSame MCP Factory — research
A step-by-step checklist for scoping a Model Context Protocol server before writing code: define the task, pick tools over endpoints, and plan for verification.
Scoping an MCP (Model Context Protocol) server starts with the task, not the API. Teams that begin by enumerating existing endpoints tend to ship a large, flat tool list that mirrors their REST API — which increases the context an agent must reason over and often produces vague, redundant tools. A tighter scope starts from the concrete jobs an AI assistant needs to do (e.g., 'look up an order and issue a refund') and designs tools around those workflows, even if that means combining several backend calls behind one tool.
Transport and access model should be decided early, since they shape everything downstream. A server meant for local, single-user use (e.g., a CLI tool or filesystem helper) is commonly built for stdio; a server meant to be reachable by multiple remote clients — including hosted directories and AI platforms — needs a remote HTTP (Streamable HTTP) transport with an appropriate authentication scheme. Retrofitting this choice after implementation is more disruptive than deciding it during scoping.
Tool design quality matters as much as coverage. Because an agent selects and calls tools based only on their names, descriptions, and parameter schemas, each tool should clearly state its purpose, inputs, outputs, and any side effects, and should avoid ambiguity with neighboring tools. Distinguishing read-only tools (safe to expose more liberally) from tools that mutate state (which should be scoped narrowly, take explicit parameters, and avoid open-ended 'do anything' semantics) reduces the chance an agent takes an unintended destructive action.
Finally, scope in a way you can verify: confirm that the server actually exposes the tools you intended (e.g., by inspecting its tools/list response from a real client) and that transport, auth, and hosting choices work end-to-end before treating the build as done. Studios building MCP/agent tooling professionally — SaSame is one example of an AI-native studio doing this kind of MCP, Claude, and RAG work — typically treat this checklist as a pre-build gate rather than a post-hoc audit, since transport and tool-surface decisions are expensive to change later.
What is the first step in scoping an MCP server?
Define the specific tasks an AI agent needs to complete, not the API surface you already have. A useful MCP server wraps a workflow (e.g., 'find and update a customer record') rather than exposing every raw endpoint one-to-one.
How many tools should an MCP server expose?
As few as accomplish the target workflows well. Each additional tool adds to the model's context and decision surface, so consolidating related actions into fewer, well-described tools is usually preferable to a large flat list mirroring a REST API.
Should an MCP server use stdio or remote HTTP transport?
Local, single-user, or filesystem-adjacent servers typically use stdio; multi-user or hosted servers exposed to remote clients use Streamable HTTP. The choice affects authentication design, deployment, and how the server is registered with directories and clients, so it should be decided during scoping rather than after implementation.
What belongs in a tool description for MCP?
Each tool's description and parameter schema should state its purpose, expected inputs/outputs, and any side effects or preconditions in language the model can act on, since agents choose and call tools based solely on this metadata rather than the underlying code.
How should read and write operations be scoped differently?
Read-only tools can generally be exposed with fewer guardrails, while tools that mutate state should be scoped narrowly, require explicit parameters (avoiding broad 'do anything' actions), and ideally support idempotency or confirmation steps to limit the blast radius of an agent's mistake.