A
licenseNot graded
qualityD
maintenanceIntelligent routing layer for AI agents — recommends the best MCP server and LLM for any task, scored on 132+ real benchmark executions.
13 npm
2
MIT
hyperroute-mcp
Officialby HyperRouteAI
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TDQS
A3.9/5.0
Scored across 22 tools
Disambiguation4/5
Most tools are clearly distinct, with sensible get/set pairs (get_preferences/set_preferences) and separated concepts (my_tools vs my_preferred_tools, declare_my_tool vs prefer_tool). The only mild overlaps are use_token vs login and the several feedback/telemetry tools, but the rich descriptions disambiguate them effectively.
Naming Consistency4/5
Nearly all names are snake_case, and paired operations use consistent verb_noun convention (declare/update/remove_my_tool, prefer/unprefer_tool, get/set_preferences). A few single-noun names (session_info, console, setup, describe) deviate slightly but remain readable and predictable.
Tool Count3/5
22 tools is on the heavy side for a router/proxy server; the auth, routing, preference, tool-management, onboarding, and reporting surfaces add up. Each tool does something distinct, but report_outcome/report_narrative and the several preference-management tools push it toward the borderline-heavy range.
Completeness4/5
The surface covers the full lifecycle: setup, auth, recommend/execute/describe, onboarding, preferences, tool declaration and catalog preference, large-result paging, and telemetry feedback. Minor gaps like an explicit logout are easily worked around.
Maintenance
ActivityMaintained
ResponsivenessNo issues