Auto-registered via benchmark URL submission.
Full LLM thinking from the 4-phase benchmark pipeline.
MCP server at https://app.mypenny.ai/mcp (endpoint source: benchmark_guide)
2/2 tests passed
| Test | Endpoint | Status | Latency |
|---|---|---|---|
| mcp_initialize | POST https://app.mypenny.ai/mcp | 200 | 735ms |
| mcp_tools_list | POST https://app.mypenny.ai/mcp | 200 | 319ms |
{
"overall": 78,
"dimensions": {
"token_efficiency": 7.0,
"first_try_success": 8.5,
"response_parseability": 8.5,
"error_clarity": 6.0,
"doc_quality": 7.5,
"auth_simplicity": 10.0,
"latency": 8.5,
"consistency": 7.5
},
"pricing_normalized": {},
"issues": [
"Tool list response preview is truncated before showing the full tool set, so token cost of discovery cannot be fully measured — the first tool description is verbose (multi-sentence) which inflates discovery tokens if similar across tools.",
"No error-path evidence: we never triggered an invalid tool call, so error_clarity is inferred rather than observed.",
"Tool response shapes for individual tool calls were not observed (only initialize and tools/list), so consistency is partially inferred.",
"No observed auth requirement (auth_wall false) — good for agents, but we cannot confirm whether write tools later demand credentials."
],
"recommendations": [
"Trim tool descriptions; move long product/context guidance into a resource (SKILL.md) referenced by the description rather than inlined.",
"Return a compact tool manifest with only name + one-line description by default, and expose full schemas on demand.",
"Ensure invalid-parameter errors return JSON-RPC error objects with a machine-readable code and a corrective hint (e.g., expected field names/types).",
"Document auth expectations explicitly so agents know whether reads are anonymous and which write tools require a token."
]
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