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genpark-multi-head-latent-attention-moe-router-skill MCP server · F · ToolBench

genpark-multi-head-latent-attention-moe-router-skill

github.com/alphaparkinc/genpark-multi-head-latent-attention-moe-router-skill

MCP server implementing a sparse Mixture of Experts (MoE) routing system with multi-head latent attention for token-to-expert dispatch optimization

2026-07-28+STDIO1 tools

17/100

Definition Quality15

Protocol Readiness5

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 server is non-functional as an MCP implementation. The mcp_server.py file contains only a print statement that outputs a JSON status message, it does NOT implement the MCP protocol. There is no tool registration, no request/response handling, no schema definitions, and no actual tool implementation. 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 stub or proof-of-concept, not a working MCP server.

Tools (1)

execute_skill_actionread onlysource verified15/100

Execute MoE routing skill action for sparse expert dispatch

Issues (5)

CRITICALtool ↗

No MCP protocol implementation. mcp_server.py only prints a static JSON message and exits. No request/response loop, no tool invocation handling, no schema validation.

execute_skill_action

CRITICALconstrained-input ↗

Tool 'execute_skill_action' has no input schema. The tool is declared in metadata but has zero parameter definitions, types, or constraints.

execute_skill_action

CRITICALtool-description ↗

Tool description is 60 characters but lacks actionable detail. Does not explain WHEN to use it, WHAT parameters it accepts, or WHAT it returns. Fails pattern:tool-description baseline (10 - 1024 chars, but must be substantive).

execute_skill_action

CRITICALresponse-shaper ↗

No output schema documented. LLMs cannot plan downstream calls or extract data without knowing the response structure. Violates pattern:tool baseline (100% of A+ tools have documented return types).

execute_skill_action

Recommendations

Implement the MCP protocol: add a request/response loop that reads JSON-RPC messages from stdin and writes responses to stdout. Use the official MCP Python SDK or implement the spec directly.
Define the input schema for execute_skill_action as a JSON Schema object with typed parameters (e.g., input_sequence_length: integer, total_experts_count: integer, active_experts_per_token: integer) and descriptions for each.
Expand the tool description to 100 - 200 characters explaining: (1) what the tool does (routes tokens to sparse experts), (2) when to use it (when optimizing MoE inference), (3) what it returns (routing IDs, compression ratios, speedup metrics).
Document the output schema explicitly, listing all returned fields (moe_routing_id, sequence_length, total_experts_pool, active_experts_dispatched, kv_cache_compression_ratio_pct, expert_load_balancing_loss, throughput_acceleration_factor) with types and descriptions.
Integrate the client.py logic into the MCP tool handler so that when execute_skill_action is invoked with parameters, it calls route_sparse_moe_tokens() and returns the structured result.

Score history

Overall score trend

First recorded score · v2 rubric

17/100

Scored	Grade	Overall	Spec posture	Rubric
2026-09-23	F	17	2026-07-28+	v2