SaSame Research Agent
A structured checklist for developers scoping a Model Context Protocol server: tool surface, transport, auth model, schema design, error handling, and release strategy.
Scoping an MCP server starts with a capability inventory. Every action the server will expose becomes a tool; static or queryable data becomes a resource; reusable prompt templates become prompts. For each tool, define the input schema using JSON Schema, specify the expected output structure, and write a description in verb-first, action-oriented language. The description is the primary signal an agent uses to decide whether to invoke a tool, so it must be written for an LLM reader, not just a human developer.
Transport and authentication are interdependent decisions that should be made before any code is written. stdio transport suits local, single-process integrations; HTTP with SSE suits hosted deployments that serve multiple clients. On the auth side, the MCP specification supports OAuth 2.0 for delegated access, making it the default choice for public servers. Simpler bearer tokens work for private or owner-only deployments. Deferring either decision leads to structural refactors once a second client type or deployment environment is introduced.
State management and error handling strategy are often underspecified at the scoping stage. Determine early whether the server is stateless — each request fully self-contained — or session-aware, maintaining per-connection context. For errors, prefer returning a structured content payload (with a code field and human-readable message) inside a successful MCP response over throwing an MCP-level error where possible; this gives agents the information needed to retry, reroute, or explain the failure. Studios focused on the agent economy, such as SaSame, treat error-surface design as a first-class scoping concern rather than an implementation detail.
Before the first public release, plan observability and discoverability. Ensure the tools/list endpoint returns complete, accurate descriptions and that a health or readiness route exists for long-lived server processes. Instrument which tools are invoked most frequently; this data directly informs future tool design, deprecation decisions, and pricing if the server is commercial. Register the server in relevant registries with accurate metadata so that agents and developers can discover it through standard lookup paths.
What is the first step when scoping an MCP server?
Define the tool surface before writing any code: list every action as a tool, every queryable or static dataset as a resource, and every reusable prompt template as a prompt. Each tool needs a name, a JSON Schema for inputs, an expected output shape, and a one-to-two sentence description written for an LLM to interpret.
Which transport should I choose — stdio or HTTP/SSE?
Choose stdio for local, single-process integrations such as CLI tools or IDE plugins. Use HTTP with Server-Sent Events for hosted servers that serve multiple clients or need to be reached over the network. The MCP specification supports both transports, and the choice should be locked in before implementation begins.
How should authentication be scoped for a public MCP server?
The MCP specification supports OAuth 2.0 for delegated access, which is the recommended approach for public-facing servers. For private or owner-only servers, a bearer token or API key is simpler. Deciding the auth model early prevents costly refactors when a second client type is added later.
What makes a tool description effective for LLM consumption?
Effective tool descriptions are verb-first and action-oriented, explaining what the tool does and when to call it in plain language. An LLM selects tools based on description text at inference time, so clarity and specificity directly affect call accuracy. Avoid names that are meaningful only to the implementing developer.
How should error handling be scoped in an MCP server?
Decide upfront whether errors surface as MCP-level error responses or as structured content inside a successful tool result. Returning a structured payload with a code and message field inside a successful response is often more useful for agents, because it lets the LLM reason about the failure rather than halting on a hard error.