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Get Started Free →Use this skill when building a Model Context Protocol (MCP) server to expose tools/resources/prompts to Atlarix (or other MCP clients), and you want correct schemas, safe handlers, and reliable local testing.
.claude/skills/amariahak-mcp-server-builder/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 25% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 75% | 0% |
| case-05 | ✓→✗ | ▼ Worse | -11% | 0% |
Use this skill when building a Model Context Protocol (MCP) server to expose tools/resources/prompts to Atlarix (or other MCP clients), and you want correct schemas, safe handlers, and reliable local testing.
MCP servers usually expose:
inputSchemaRules:
inputSchema is strict enough to prevent garbage inputValidate input even if schema exists—treat schema as first line of defense.
Rules:
{ success: false, error: "..." } where appropriateExample result shape:
tsreturn { success: true, result: { id: "123", status: "ok" } };
Pattern:
resource://logs/{id}text/plain, application/json, etc.Avoid:
Use stdio when:
Use HTTP when:
Run locally and verify:
Common errors:
{} but server expects string)If a tool can mutate state (filesystem, DB, deployments):
When you change a tool:
Recommended development loop:
When designing tools for Atlarix agents:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 3,000 | 2,554 | -15% | 1 | 1 | 0% | 431 | 1,187 | +175% | 0 | 0 | — |
case-02 | pass→pass | 2,104 | 2,637 | +25% | 1 | 1 | 0% | 309 | 1,199 | +288% | 0 | 0 | — |
case-03 | pass→pass | 2,823 | 1,988 | -30% | 1 | 1 | 0% | 476 | 1,124 | +136% | 0 | 0 | — |
case-04 | fail→pass | 12,584 | 11,174 | -11% | 1 | 1 | 0% | 2,407 | 3,010 | +25% | 0 | 0 | — |
case-05 | pass→fail | 9,824 | 6,320 | -36% | 1 | 1 | 0% | 1,768 | 1,578 | -11% | 0 | 0 | — |
case-06 | pass→pass | 7,213 | 5,976 | -17% | 1 | 1 | 0% | 1,173 | 1,825 | +56% | 0 | 0 | — |
case-07 | pass→pass | 11,402 | 8,240 | -28% | 1 | 1 | 0% | 1,899 | 2,083 | +10% | 0 | 0 | — |
case-08 | pass→pass | 12,652 | 5,407 | -57% | 1 | 1 | 0% | 2,087 | 1,573 | -25% | 0 | 0 | — |
case-09 | pass→pass | 3,911 | 3,693 | -6% | 1 | 1 | 0% | 669 | 1,328 | +99% | 0 | 0 | — |
case-10 | pass→pass | 10,336 | 6,714 | -35% | 1 | 1 | 0% | 1,882 | 2,102 | +12% | 0 | 0 | — |
case-11 | pass→pass | 12,593 | 9,349 | -26% | 1 | 1 | 0% | 2,071 | 2,497 | +21% | 0 | 0 | — |
case-12 | pass→pass | 14,035 | 10,824 | -23% | 1 | 1 | 0% | 2,391 | 2,813 | +18% | 0 | 0 | — |
case-13 | pass→pass | 5,602 | 2,906 | -48% | 1 | 1 | 0% | 961 | 1,308 | +36% | 0 | 0 | — |
case-14 | pass→pass | 11,152 | 7,381 | -34% | 1 | 1 | 0% | 1,857 | 2,013 | +8% | 0 | 0 | — |
case-15 | fail→pass | 10,670 | 7,903 | -26% | 1 | 1 | 0% | 1,878 | 2,046 | +9% | 0 | 0 | — |
case-16 | pass→pass | 10,422 | 7,492 | -28% | 1 | 1 | 0% | 1,671 | 2,100 | +26% | 0 | 0 | — |
case-17 | pass→pass | 13,116 | 6,775 | -48% | 1 | 1 | 0% | 2,054 | 1,883 | -8% | 0 | 0 | — |
case-18 | fail→pass | 8,419 | 1,782 | -79% | 1 | 1 | 0% | 1,240 | 1,002 | -19% | 0 | 0 | — |
case-19 | pass→pass | 13,768 | 15,415 | +12% | 1 | 1 | 0% | 2,386 | 2,805 | +18% | 0 | 0 | — |
case-20 | fail→pass | 6,157 | 5,518 | -10% | 1 | 1 | 0% | 1,100 | 1,929 | +75% | 0 | 0 | — |
case-21 | pass→pass | 4,999 | 3,777 | -24% | 1 | 1 | 0% | 721 | 1,312 | +82% | 0 | 0 | — |
case-22 | pass→pass | 8,249 | 6,388 | -23% | 1 | 1 | 0% | 1,517 | 1,876 | +24% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +14 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.