A
licenseNot graded
qualityD
maintenanceMCP server that benchmarks AI models on your actual prompts and finds cheaper, faster alternatives.
29 npm
MIT
model-radar
by srclight
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TDQS
A4.2/5.0
Scored across 21 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: ask runs prompts on multiple models, run executes a single prompt, judge evaluates, compare does A/B comparison, etc. Even similar tools like scan and get_fastest are differentiated by description (scan pings and sorts all, get_fastest returns top N). No ambiguity.
Naming Consistency5/5
All tool names use lowercase with underscores for compound names (e.g., batch_judge, list_models, configure_key). Single-word names are verbs (ask, run, scan). No mixing of camelCase or other conventions, making the pattern predictable.
Tool Count4/5
21 tools is slightly high but justified by the server's broad scope: querying, evaluation, benchmarking, management, configuration, and maintenance. Each tool earns its place, and the count does not feel excessive for the functionality offered.
Completeness4/5
The tool surface covers model querying, evaluation (multiple methods), discovery, configuration, and server management. It supports translation evaluation via backtranslate_eval and benchmarking. Minor gaps like a model deletion tool are not critical for the domain.
Maintenance
ActivityMaintained
ResponsivenessNo issues