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genpark-autonomous-meta-skill-synthesizer-self-evolution-skill MCP server · F · ToolBench

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genpark-autonomous-meta-skill-synthesizer-self-evolution-skill

github.com/alphaparkinc/genpark-autonomous-meta-skill-synthesizer-self-evolution-skill

An MCP server that synthesizes autonomous agent skills through meta-skill generation and self-evolution

2026-07-28+STDIO1 tools

13/100

Definition Quality8

Protocol Readiness2

Supportability28

Spec posture

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

Static source inference · medium confidence

Current-spec patterns detected

Summary

This MCP server is non-functional. The source code shows no actual MCP server implementation. The 'mcp_server.py' file contains a print statement that outputs JSON, not a legitimate Model Context Protocol server. There is no tool schema registration, no proper request/response handling, no input validation, and the 'execute_skill_action' tool is declared in the feature summary but never actually defined or registered anywhere in the codebase. The 'client.py' and 'example_usage.py' files are standalone Python classes completely unrelated to MCP protocol implementation. This appears to be a demonstration of fictional autonomous skill synthesis marketing copy, not a real MCP server.

Tools (1)

execute_skill_actionwritesource verified8/100

Execute a skill action within the autonomous meta skill synthesizer

Issues (7)

CRITICALtool ↗

Tool schema is completely missing. The tool 'execute_skill_action' is mentioned only in a JSON string printed to stdout; there is no actual input schema, output schema, or parameter definition anywhere in the codebase.

execute_skill_action

CRITICALtool-description ↗

Tool description is vague and non-actionable (20 chars). 'Execute a skill action within the autonomous meta skill synthesizer' provides no detail on what inputs are accepted, what the tool returns, when to use it vs. alternatives, or how to recover from errors.

execute_skill_action

CRITICALprotocol-basics ↗

No actual MCP server implementation. The 'mcp_server.py' file merely prints a static JSON message to stdout and exits. There is no message loop, no request parsing, no tool invocation handler, and no protocol compliance. This cannot communicate with any MCP client.

CRITICALconstrained-input ↗

Tool parameters are completely undocumented. No input schema visible, no parameter descriptions, no type information, no constraints, and no examples. An LLM cannot determine what arguments execute_skill_action accepts.

Recommendations

Implement an actual MCP server with a proper request/response loop that parses JSON-RPC 2.0 messages from stdin and writes responses to stdout.
Define the execute_skill_action tool with a complete JSON Schema input object specifying all required and optional parameters, their types, descriptions, and constraints (enums, patterns, ranges).
Add a clear, LLM-optimized description for execute_skill_action (50 - 200 chars) explaining: What does it do? When should it be called? What does it return? Example: 'Execute a predefined skill within the meta-synthesizer. Returns the execution result with status, generated artifacts, and validation metrics.'
Document the output schema with all returned fields, their types, and meanings. Include any IDs or references needed for downstream tool calls.
Add comprehensive parameter descriptions. For each input parameter (e.g., skill_id, capability_spec, generation_target), explain its purpose, valid values, format, and dependencies.
Implement error handling that returns structured error objects with: error code, human-readable message, the invalid value (if applicable), the constraint violated, and a suggested recovery action (e.g., 'Try list_available_skills() first').
Remove or repurpose client.py and example_usage.py. If they serve as documentation, move their content to README.md. If they are unused demo code, delete them.
Ensure the server declares all supported tools, their schemas, and capabilities in the initialize response per the current MCP 2026-07-28 spec.

Score history

Overall score trend

First recorded score · v2 rubric

13/100

Scored	Grade	Overall	Spec posture	Rubric
2026-09-23	F	13	2026-07-28+	v2