A
licenseNot graded
qualityB
maintenanceEnables local CLI and desktop MCP clients to control the MazeBench 3D game engine via stdio, with real-time browser viewing, action recording, and post-run summaries and replays.
MIT
MarketCanvas-Env MCP Server
by Rong-Ding
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TDQS
A3.9/5.0
Scored across 5 tools
Disambiguation5/5
Each tool has a clearly distinct target: get_canvas_state reads world state, get_action_schema reads the action spec, get_current_reward reads scoring diagnostics, execute_action mutates, and reset_environment restarts. The three 'get_' readers could superficially look similar but their nouns (state/schema/reward) are unambiguous. No two tools overlap in purpose.
Naming Consistency5/5
All five tools follow a clean snake_case verb_noun pattern (get_canvas_state, get_action_schema, execute_action, get_current_reward, reset_environment). The convention is uniform across readers, mutators, and lifecycle ops. No mixing of styles.
Tool Count5/5
Five tools is exactly right for an environment-style server: observe, inspect schema, act, preview reward, reset. Nothing is redundant and nothing feels missing at the count level. Well-scoped and each tool earns its place.
Completeness5/5
The surface covers the full interaction lifecycle: reset/init, state observation, action schema discovery, action execution (add/move/update/delete/submit), and reward feedback. This is a complete observe-act-reward loop with no obvious dead ends.
Maintenance
ActivityMaintained
ResponsivenessNo issues