Critical failures across all dimensions. The single tool 'execute_skill_action' has a vague, generic description (58 chars) that does not explain WHAT the tool does, WHEN to use it, or what it returns. Parameters lack type definitions in the visible schema, 'workflow_dag_json' and 'execution_priority' are declared as strings with defaults but no validation rules, enums, or constraints. No output schema is documented anywhere in the codebase. The tool name uses 'execute' + 'action', which is generic and does not convey the specific operation. No error handling, no recovery guidance, no security considerations. The implementation is a stub that prints JSON and returns mock data, not a real MCP server.

• Rewrite the tool description to 100 - 200 characters explaining: (1) what it does ('Executes a directed acyclic graph of compute nodes'), (2) when to use it ('when you need to run a multi-stage workflow'), (3) what it returns ('execution ID, node count, VRAM usage, and output artifact URLs'). Example: 'Execute a modular node graph DAG workflow. Runs a sequence of compute nodes (e.g., UNetLoader, CLIPTextEncode, KSampler, VAEDecode) in topological order. Returns execution ID, node count, peak VRAM, and output artifact URLs.'
• Add explicit JSON Schema for input parameters. Define 'workflow_dag_json' as a string with a pattern or format constraint (e.g., 'valid JSON object with a "nodes" array'). Define 'execution_priority' as an enum: ['LOW', 'MEDIUM', 'HIGH', 'CRITICAL']. Add descriptions to each parameter explaining what it controls.
• Document the output schema with typed fields: 'dag_execution_id' (string), 'nodes_evaluated_count' (integer), 'vram_peak_allocation_gb' (number), 'intermediate_latents_cached' (boolean), 'topological_execution_time_seconds' (number), 'output_artifacts_urls' (array of strings). Include this in the tool definition or a separate schema document.
• Rename the tool to 'execute_dag_workflow' or 'run_node_graph' to be more specific and follow verb_noun convention. This makes the tool's purpose immediately clear to LLMs.
• Add error handling with recovery guidance. Example: 'If a node fails, return error code and the name of the failing node. Suggest: "Node CLIPTextEncode failed. Check that the model path is valid and VRAM is sufficient."' Include error classification (retryable vs. fatal).