ScoreFrameworkRankingPricingBlog GitHub

ai-103

learnnaga/ai-103

88

Dominant

1 tool · Source: smithery · Scanned: April 26, 2026

Score breakdown

Findability

25/25

Can AI agents discover this tool? Measures naming quality, keyword overlap with queries, and registry presence.

Clarity

30/35

Can agents understand what this tool does? Measures description quality, action verbs, usage context ("Use this when..."), and return value documentation.

Precision

18/25

Is the interface precise? Measures input schema completeness, type definitions, required fields, enums, defaults, and parameter descriptions.

Efficiency

15/15

Is the tool token-efficient? Measures description length optimization, tool count, and naming conciseness.

Optimization status

Precision

Define required fields in your inputSchema to prevent agents from guessing parameters

Expected impact: +2-3 pts

Selection Win Rate

100%

Measured via 100-round LLM selection tournament. Claude Sonnet was presented 4 competing tools per round and asked to select the best for a realistic task. This server won 100% of rounds it appeared in.

Layer 2: Real LLM selection data. Methodology →

Trust Verification

1Spec Verified
📋Spec Verified○ Not yet
88/100Spec Score
RequirementScore ≥ 85 on ToolRank Spec Quality analysis
MethodAutomated static analysis of tool definitions, descriptions, parameters, error handling
RenewalRe-verified on every scan (weekly)
🎯Selection Verified✓ Verified
100%Win Rate
RequirementSelection win rate ≥ 70% in 100-round LLM tournament
MethodClaude Sonnet selects best tool for realistic tasks vs. competing servers
RenewalRe-tested monthly; expires if win rate drops below 60%
VerifiedMar 31, 2026
⚡Runtime Verified○ Not yet
—Status
RequirementTask success rate ≥ 90%, error rate < 5%, avg latency < 2s
MethodEnd-to-end execution tests in sandboxed environment
RenewalContinuously monitored; revoked on sustained failure
View full audit log → ![Trust](https://mcp.toolrank.dev/api/trust-badge/086f1626-9d7d-409d-aa9a-edd00ded393a.svg)

Score history

88
Mar 28
88
Mar 28
88
Mar 29
88
Mar 29
88
Mar 29
88
Mar 30
88
Mar 31
88
Apr 1
88
Apr 2
88
Apr 3
88
Apr 4
88
Apr 5
88
Apr 6
88
Apr 7
88
Apr 8
88
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88
Apr 11
88
Apr 12
88
Apr 13
88
Apr 14
88
Apr 15
88
Apr 18
88
Apr 23
88
Apr 24
88
Apr 25
88
Apr 26

Tracked daily. 26 data points.

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