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MCP Academy · Lesson

Shape Messages & Preferences

Set system text and model hints for the call.

Shape Messages & Preferences is a free MCP Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MCP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

You Steer the Generation

Beyond the prompt, you can shape the call: a system prompt, model hints, length, and how much past context to include.

Set the System Prompt

Use systemPrompt to set the model’s role or tone for this one call, like telling it to be a terse code reviewer.

"systemPrompt": "You are a concise code reviewer."

Stack Multiple Messages

The messages array can hold several turns, letting you give the model a short back-and-forth as context, not just one line.

Suggest a Model with Hints

modelPreferences takes hints — substrings like claude-3-sonnet — that the client may match to an available model.

"hints": [{ "name": "claude-3-sonnet" }]

Hints Are Only Advisory

The client makes the final pick. A hint is a suggestion; the host may map it to an equivalent model from another provider.

Fall Back Gracefully

List hints in order of preference. A broad fallback like a bare claude hint catches any Claude model if your first pick is missing.

"hints": [{ "name": "claude-3-sonnet" },
          { "name": "claude" }]

Prioritize Intelligence

Set intelligencePriority from 0 to 1. Higher values nudge the client toward more capable, smarter models for hard work.

"intelligencePriority": 0.8

Prioritize Speed

Need low latency? Raise speedPriority toward 1 and the client leans on faster models, trading some depth for quickness.

"speedPriority": 0.8

Prioritize Cost

Raise costPriority to prefer cheaper models. These three priorities are normalized hints, not hard rules.

"costPriority": 0.3

Include Surrounding Context

An includeContext setting lets the request pull in context from MCP servers, so the model sees more than just your text.

Tune Temperature

Where supported, a temperature value shifts replies from focused and deterministic to more varied and creative.

Quick Check

One question on guiding model choice.

Recap

Shape calls with a systemPrompt, layered messages, and modelPreferences — hints plus cost, speed, and intelligence priorities. 🎛️

Frequently asked questions

Is the “Shape Messages & Preferences” lesson free?

Yes — the full text of “Shape Messages & Preferences” is free to read here on the web, and the MCP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MCP Academy course, upgrade to CoddyKit PRO.

What will I learn in “Shape Messages & Preferences”?

Set system text and model hints for the call. You practise MCP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start MCP Academy?

No prior experience is required. MCP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Shape Messages & Preferences” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this MCP Academy lesson?

Yes. Every MCP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. What Sampling Unlocks
  2. Request a Completion from the Host
  3. Shape Messages & Preferences
  4. Human-in-the-Loop Approval
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