This MCP server has critically deficient tool definition quality. The single tool 'execute_skill_action' lacks an input schema entirely, has a minimal description (20 chars), and no visible parameter definitions. The server implementation (mcp_server.py) does not actually register tools with the MCP protocol, it only prints a JSON status message to stdout. There is no request/response handling, no tool invocation logic, and no schema definitions. The 'client.py' module contains a standalone SDK unrelated to the MCP server. This is not a functional MCP server implementation.
Tool description is only 20 characters ('Execute schema markup generation skill action'), below minimum clarity threshold. Does not explain WHEN to use this tool, what parameters it accepts, or what it returns.
mcp_server.py does not implement MCP server protocol correctly. It only prints a status message to stdout once and exits. There is no request/response loop, no tool invocation handler, and no error handling.
execute_skill_action
Recommendations
Define a complete JSON Schema for execute_skill_action input. At minimum: property names (e.g., 'skill_name', 'parameters'), types (string, object, etc.), and whether each is required. Example: {"type": "object", "properties": {"skill_name": {"type": "string", "description": "The AEO skill to execute (e.g., 'schema_markup_generator')"}, "parameters": {"type": "object", "description": "Skill-specific input (e.g., product_name, price_usd, sku)"}}, "required": ["skill_name", "parameters"]}
Expand tool description to 50-200 characters explaining: (1) What this tool does ('Executes AEO entity schema markup generation'), (2) When to use it ('Use this to generate JSON-LD schema markup for search engine optimization'), (3) What it returns ('Returns a structured JSON-LD schema object').
Add parameter descriptions for each input. For example, if 'skill_name' is a parameter, document it as: 'skill_name (string, required): Name of the skill to execute. Valid values: schema_markup_generator, entity_enrichment. Controls which transformation logic runs.'
Rewrite mcp_server.py to implement the full MCP STDIO protocol. Use a standard MCP server library (e.g., mcp Python SDK) or implement the JSON-RPC 2.0 message loop manually: (1) Read JSON requests from stdin, (2) Parse the method (tools/call, resources/read, etc.), (3) Route to handler functions, (4) Return JSON responses on stdout. Example skeleton: while True: request = json.loads(sys.stdin.readline()); handle_request(request); sys.stdout.write(json.dumps(response)).
Client SDK (client.py) is disconnected from the MCP server. It contains a standalone AEOEntitySchemaMarkupClient class with a generate_schema() method, but this is never wired into the MCP tool registration or invocation.
No error handling or recovery guidance. If the skill execution fails, there is no structured error response or hint for what the LLM should try next.
execute_skill_action
Connect client.py to the MCP server. Replace the standalone AEOEntitySchemaMarkupClient.generate_schema() with a proper MCP tool handler that accepts the schema parameters and calls client.py internally. Register execute_skill_action with schema that matches what client.py.generate_schema() expects (product_name, price_usd, sku).
Document the output schema. Add a response example and schema definition. For example: {"type": "object", "properties": {"json_ld_schema": {"type": "string", "description": "JSON-LD schema as formatted string"}, "success": {"type": "boolean"}}, "required": ["json_ld_schema", "success"]}.
Implement error handling with recovery hints. If skill_name is invalid, return: {"error": "Unknown skill 'invalid_name'. Try one of: schema_markup_generator, entity_enrichment."}. If parameters are missing, return: {"error": "Missing required parameter 'product_name' for skill schema_markup_generator."}.