MCP server for building responsive interactive web card presentations with embedded widgets and PDF export capabilities
This server has a single tool 'execute_skill_action' with virtually no implementation. The tool name is vague (execute_skill_action is a generic verb+noun that does not convey what action is executed). No input schema is visible in the source code, the mcp_server.py only prints a JSON status message and does not register tools with schemas. No parameter descriptions exist. The tool description is minimal (31 chars) and does not explain WHAT the skill does, WHEN to use it, or what it returns. The client.py and example_usage.py files show a presentation-building capability, but this is NOT wired into the MCP server at all, the server does not call or expose this functionality. The server is essentially a stub that announces it supports a tool but provides no actual tool definition, schema, or implementation.
Execute responsive interactive web card presentation skill action
No input schema visible for execute_skill_action. Tool registration does not include JSON Schema with parameter types, descriptions, or constraints.
Tool description is 31 characters ('Execute responsive interactive web card presentation skill action'), below the 50-char minimum for LLM clarity. Does not answer WHAT the tool does, WHEN to use it, or what it returns.
Tool name 'execute_skill_action' is generic and vague. Does not start with a clear action verb (get, create, update, delete, search, list, send). 'execute' + 'skill_action' does not convey what the tool actually does. LLMs cannot infer intent from this name.
Mismatch between server and client implementation. The mcp_server.py announces support for 'execute_skill_action' but does not actually implement it or wire it to the ResponsiveInteractiveWebCardPresentationClient in client.py. The server is a non-functional stub.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 22 | 2026-07-28+ | v2 |
No parameters documented. The tool accepts unknown inputs with no schema, type information, or descriptions. LLMs cannot determine what parameters to pass or what values are valid.