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AI Engineering · Version 1.3.0 · Reviewed 2026-08-02

MCP Server Architecture Designer

Make AI behavior measurable and safer for MCP tool contract design and MCP transport selection with evidence, explicit trade-offs, and a verification plan.

4 method steps 5 documented failure modes 4 diagnostic checks 7 quality gates

Designs portable Model Context Protocol servers with explicit tools, resources, authentication, transport, and operational boundaries. It grounds the decision in host capabilities, tool schemas, data sensitivity, caller identity, transport needs, and deployment constraints and explicitly prevents a broad tool contract exposing destructive authority or returning success-shaped results when an upstream call fails.

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What this skill helps you do

  • MCP tool contract design
  • MCP transport selection
  • MCP production hardening

How MCP Server Architecture Designer works

You provide

Prompts, model versions, evaluation data, and observed failures

It inspects

Failure class and context sufficiency for MCP tool contract design

It decides

A MCP transport selection change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

MCP Server Architecture Designer designs portable Model Context Protocol servers with explicit tools, resources, authentication, transport, and operational boundaries. It grounds the decision in host capabilities, tool schemas, data sensitivity, caller identity, transport needs, and deployment constraints and explicitly prevents a broad tool contract exposing destructive authority or returning success-shaped results when an upstream call fails. Use it when the work involves MCP tool contract design, MCP transport selection, MCP production hardening.

  1. Whether failures are systematic across an input class or random, which separates capability from sampling.
  2. Whether evaluation data overlaps the data used to build the prompt or model.
  3. Version pinning across model, prompt, retrieval, and tools so regressions are attributable.
  4. Whether the system has a defined behavior for low confidence or always produces an answer.

Failure modes it recognizes

  • A silent quality regression after a provider updates a model behind an unversioned alias.
  • Training and serving computing a feature differently, producing an offline-to-online gap.
  • Evaluation overfitting because the prompt was tuned on the scoring examples.
  • Confident fabrication when context is insufficient because no refusal path exists.
  • Cost and latency dominated by a small tail of very long inputs absent from the test set.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a small regression.
  • Using a larger model to compensate for missing context, paying more for the same failure.
  • Using a model as judge without validating it against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled set and a baseline before changing anything.
  • Pin every version and change one component at a time.
  • Define and test the refusal path; a system that cannot decline will fabricate.

Evidence it asks for

  • Score per input class so an aggregate score cannot hide a broken class.
  • Log model, prompt, and retrieval versions on every request.
  • Track p95 tokens and cost per successful task rather than per call.

The method inside

  1. Turn MCP tool contract design into explicit functional requirements and quality-attribute constraints.
  2. Model the critical path, state, trust, and failure boundaries that govern MCP transport selection.
  3. Compare viable designs for MCP production hardening against weighted constraints and operational ownership.
  4. Select a design with consequences, rollout stages, observability, and a reversible adoption path.

Deliverables

  • MCP tool contract design assessment
  • MCP transport selection decision and action plan
  • MCP production hardening verification checklist

Evidence requirements

  • Prompts, model/version, tools, retrieval path, and examples
  • Evaluation dataset and failure cases
  • Latency, cost, privacy, and policy constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Apply the mcp server architecture designer to our current mcp tool contract design work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with host capabilities, tool schemas, data sensitivity, caller identity, transport needs, and deployment constraints. The highest-risk failure is a broad tool contract exposing destructive authority or returning success-shaped results when an upstream call fails. Prefer narrow typed operations, explicit error contracts, least privilege, and a transport chosen from real host requirements. Verify the result by exercising schema errors, authorization failures, cancellation, timeouts, and replay behavior from an independent MCP client.

Boundaries and compatibility

Ideal for

  • MCP tool contract design: produce a decision or artifact grounded in supplied evidence.
  • MCP transport selection: produce a decision or artifact grounded in supplied evidence.
  • MCP production hardening: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Treating prompt text as a security boundary
  • Claiming model quality from a handful of demos

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.