MCP server for onboarding electric two-wheeler drivers and vehicles into a fleet management system with battery swap station integration and telematics tracking
Single tool 'execute_skill_action' has a basic description (67 chars, within 10-1024 range) but lacks critical depth. Input schema is present with two string parameters, both with descriptions. However, the tool definition is inferred from client.py rather than explicitly registered in mcp_server.py, the actual MCP server code contains only a JSON status message with no tool registration, schema validation, or error handling. Output schema is completely undocumented. No error guidance, no idempotency hints, no permission gates. The tool name 'execute_skill_action' is generic and violates verb_noun clarity (should be 'onboard_ev_driver' or similar). No evidence of input validation, rate limiting, or security controls.
Execute fleet onboarding skill action for two-wheeler EV drivers
Tool definition not explicitly registered in MCP server code. mcp_server.py contains only a static JSON status message; actual tool logic is in client.py and never wired into the MCP protocol handler.
No output schema documented. Tool returns a complex object with 7 fields (driver_id, vehicle_type, battery_state_of_health_pct, iot_telematics_connected, vehicle_financing_rent_to_own_daily_ngn, nearest_battery_swap_station, battery_swap_time_seconds, driver_safety_telematics_score) but LLM has no schema to parse or validate response structure.
Generic tool name 'execute_skill_action' violates verb_noun clarity. Does not convey the actual action (onboarding an EV driver to a fleet). LLM cannot infer intent from name alone. Should be 'onboard_ev_driver' or 'register_driver_to_station'.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 30 | 2026-07-28+ | v2 |
No error handling or recovery guidance. Tool description does not explain failure modes, retryability, or what to do if driver_national_id is invalid or battery_swap_station_id does not exist. No error classification (retryable vs user-fixable vs fatal).
No input validation or constraints. Parameters accept arbitrary strings with no enum, regex, or length limits. LLM can pass malformed IDs (e.g., 'NIN_invalid' or 'BSS_xyz') with no guidance on valid formats.