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genpark-open-saas-enterprise-composable-headless-skill MCP server · F · ToolBench

genpark-open-saas-enterprise-composable-headless-skill

github.com/alphaparkinc/genpark-open-saas-enterprise-composable-headless-skill

MCP server for deploying composable storefront APIs for enterprise merchants with multi-region support and B2B capabilities

2026-07-28+STDIO1 tools

16/100

Definition Quality15

Protocol Readiness5

Supportability25

Spec posture

Inferred effective spec 2026-07-28+·current baseline 2026-07-28

Static source inference · medium confidence

Current-spec patterns detected

Summary

This server is non-functional as an MCP implementation. The mcp_server.py file contains only a single print statement that outputs a JSON status message, it does NOT implement the MCP protocol. There is no tool registration, no input/output schema, no parameter handling, and no actual tool invocation logic. The 'execute_skill_action' tool is declared in the metadata but has zero implementation. The client.py and example_usage.py files are standalone Python code unrelated to MCP. This is a shell project, not a working MCP server.

Tools (1)

execute_skill_actionwritesource verified15/100

Execute a skill action within the GenPark composable headless system

Issues (5)

CRITICALtool ↗

No MCP protocol implementation. mcp_server.py contains only a print() statement; does not handle MCP requests, parse JSON-RPC, or invoke tools.

execute_skill_action

CRITICALconstrained-input ↗

Tool 'execute_skill_action' has no input schema. Cannot verify parameter types, required fields, or constraints.

execute_skill_action

HIGHtool-description ↗

Tool description is 67 characters but lacks actionable detail. Does not explain WHAT action is executed, WHEN to use it, or what parameters it accepts.

execute_skill_action

HIGHresponse-shaper ↗

No output schema documented. LLMs cannot predict what fields the tool returns or plan downstream calls.

execute_skill_action

HIGHrecovery-guide ↗

No error handling or recovery guidance. Tool will fail silently or crash without telling the LLM what to do next.

Recommendations

Implement the MCP protocol: add JSON-RPC 2.0 request/response handling, tool registration with schemas, and proper stdio communication.
Define the input schema for execute_skill_action with required parameters, types, descriptions, and constraints (use JSON Schema format).
Expand tool description to 100-200 characters explaining: what skill action is executed, what parameters control it, and what the response contains.
Document the output schema with all returned fields, their types, and what they represent.
Add error handling that returns actionable messages: 'Skill not found. Available skills: [list]. Try search_skills() first.' instead of silent failures.

Score history

Overall score trend

First recorded score · v2 rubric

16/100

Scored	Grade	Overall	Spec posture	Rubric
2026-09-23	F	16	2026-07-28+	v2