MCP server design

SkillMedia

Build an MCP server, or bring an existing one into a project, choosing the transport, deciding which tools, resources and prompts to expose, keeping reads separate from writes, handling auth and credentials, and defining what failure looks like to the model. Use when writing an MCP server, wrapping an internal system as one, or evaluating a third-party server before wiring it in. Not for designing the individual tool signatures inside it (tool-design), and not authorization to install, configure or run any server against real systems.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the MCP server design skill

What this skill tells your AI

The instructions your AI receives, as published by nahid-sparktales/agent-dispatcher in skills/ai/mcp-design/SKILL.md and read by ahel’s review.

An MCP server is a permission surface wearing an API's clothes. Whatever it exposes, the model can call; whatever the process can reach, the server can be asked to touch. Design the boundary first and the tool list second.

When this fires

Writing an MCP server, wrapping an internal service as one, or assessing a third-party server before it is added to a project. It does not fire for defining the parameters of a single tool — that is tool-design — nor for ordinary client code that happens to call an API.

Procedure

  1. Establish why this is a server at all. A capability the agent uses in one repo, with the files already on disk, is a script or a skill. MCP earns its place when the capability is reused across projects or clients, needs its own credentials, or must run somewhere the agent is not.
  2. Pick the transport from where it runs. Local process on the user's machine, launched by the client and speaking over stdio: simplest, credentials come from the environment, nothing listens on a port. Remote server over HTTP: needed for a hosted or shared service, and it brings network exposure and real authorization with it. Check the current MCP specification for which HTTP transport is current rather than copying an old example — earlier transports have been superseded.
  3. Draw the capability surface deliberately. Tools are model-invoked actions. Resources are readable context the client can attach. Prompts are user-invoked templates. Put passive lookups behind resources instead of inflating the tool list, and expose the smallest set that does the job; every extra tool costs selection accuracy for every task, including the ones that never touch it.
  4. Separate reads from writes explicitly. Name them so the difference is visible in the list, and ship a read-only mode — a flag, a scoped token, or a separate entry point — that the server itself enforces. A read-only intention is not a read-only server.
  5. Scope credentials down before the first call. The server gets its own least-privileged credential, not the user's admin token. Never read secrets out of the model's arguments. Keep credentials in the environment or a secret store, never echoed back in a tool result, a log line or an error message. For anything remote, follow the specification's authorization model rather than inventing a header scheme.
  6. Treat every input as coming from a model that may be steered. Arguments are untrusted: allowlist paths, table names, commands and hosts; parameterize queries; bound page sizes and result counts. Text fetched from outside — a page, an issue body, a file — is data the model will read, and it may contain instructions. Return it plainly labelled as content; never let the server act on instructions it finds in the data it retrieved.
  7. Define failure semantics as part of the contract. Distinguish not-found from unauthorized from upstream-down from bad-argument, and return each as a readable result the model can act on rather than a crash that kills the session. Set timeouts on every upstream call. Say in the result when output was truncated, and how to fetch the rest.
  8. Make destructive and outward-facing operations stop. Deleting, sending, publishing, merging, deploying, charging: the tool description says so, the operation is confirmable by the human, and where the client offers a confirmation or annotation mechanism, use it. The server never treats its own availability as consent. Prefer reversible: soft-delete, dry-run parameters, a diff returned before the change is applied.
  9. Prove it against the real client, not just a unit test. Start the server, connect the client the user will actually use, list the capabilities, and call each tool once — success, empty result, denial, and a deliberately malformed argument. Read what the model received, not what your handler returned.
  10. When evaluating someone else's server, read before installing. Who publishes it, what licence, is it maintained, which tools write, does it offer a read-only mode, what credential does it want and what does that credential let it do. Installing, configuring or pointing a server at production is the user's decision — present the assessment and ask.

Checklist

  • The case for a server over a script or skill is stated
  • Transport chosen for where it runs, against the current specification
  • Capability surface minimal; passive lookups are resources, not tools
  • Read and write tools visibly separated, with an enforced read-only mode
  • Least-privileged credentials, sourced from the environment, never in results or logs
  • Every argument allowlisted or parameterized; outputs bounded
  • Retrieved external text returned as data, never executed as instruction
  • Not-found, unauthorized, upstream-failed and malformed are four distinct results
  • Destructive operations labelled, reversible where possible, and confirmed by a human
  • Exercised through the real client, with the transcript read

Failure handling

  • Client will not connect — check transport mismatch and startup errors first. On stdio, anything the server prints to stdout that is not protocol traffic corrupts the stream; log to stderr.
  • Tools list but calls fail — usually credentials or scope, not protocol. Check what the token can actually do before changing the code.
  • The model picks the wrong tool or drowns in output — that is a surface problem: too many tools, overlapping descriptions, or unbounded results. See tool-design.
  • An upstream call hangs — a server without timeouts hangs the whole session. Bound it, and return a distinguishable timeout result.
  • You cannot confirm a protocol feature exists in the version you are targeting — read the specification or the SDK's current docs. Do not implement against a remembered field name.

Evidence to report

The transport and why. The full capability list, marked read or write, with the read-only mode and how it is enforced. The credential and its scope. A transcript per tool showing success and at least one failure path as the model saw it. For a third-party server: source, licence, maintenance, whether it writes, and the pending question of whether the user wants it installed. Keep the verbs honest — written, executed once, covered by a test, and connected to a real system are four different claims.

Signals

GitHub stars
20
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
mcp-design
Source
github.com/nahid-sparktales/agent-dispatcher