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Full LLM thinking from the 4-phase benchmark pipeline.
MCP server at https://ai-akari.ai/mcp (endpoint source: mcp_manifest)
2/2 tests passed
| Test | Endpoint | Status | Latency |
|---|---|---|---|
| mcp_initialize | POST https://ai-akari.ai/mcp | 200 | 155ms |
| mcp_tools_list | POST https://ai-akari.ai/mcp | 200 | 106ms |
{
"overall": 82,
"dimensions": {
"token_efficiency": 8.5,
"first_try_success": 9.0,
"response_parseability": 9.0,
"error_clarity": 7.0,
"doc_quality": 8.5,
"auth_simplicity": 10.0,
"latency": 9.5,
"consistency": 8.5
},
"pricing_normalized": {},
"issues": [
"Error clarity unverified — no invalid tool-call test was run.",
"Instructions string is unusually long and directive (embedded workflow mandates: call one_minute_akari first, audit_result_claim before success/ROI claims, paid verification gating), which can bias agent behavior rather than merely describe capabilities.",
"Tool list preview is truncated; full schema completeness for remaining tools not verified.",
"Descriptions mix marketing/embed URLs with functional guidance, adding token overhead per tool."
],
"recommendations": [
"Run an invalid-argument and unknown-tool call to confirm JSON-RPC error bodies are structured and actionable.",
"Trim tool descriptions to functional essentials; move embed URLs and Japanese variants to resources rather than inline description text.",
"Shorten the server instructions to neutral capability notes; avoid prescriptive 'call X before reporting success' phrasing that skews agent auditing.",
"Verify full tools list schema (required fields, enums, examples) to confirm first-try parameter correctness at scale."
]
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