Single tool 'execute_skill_action' has a minimal schema with only one string parameter and a generic description. The tool name does not follow verb_noun convention (should be 'validate_gguf_header' or similar). Description (95 chars) lacks specificity about what the tool actually does, when to use it, and what it returns. No output schema is documented. The actual GGUF validation logic exists in client.py but is not exposed as a callable tool, only a generic echo endpoint is registered. This is a critical mismatch: the server advertises a skill but does not implement the skill's core functionality.

• Rename 'execute_skill_action' to 'validate_gguf_header' or 'verify_tensor_metadata' to clearly signal the tool's purpose.
• Rewrite the tool description to explain: (1) What it does: 'Validates GGUF binary headers and verifies tensor metadata completeness'; (2) When to use it: 'Call this to verify a model file is a valid GGUF format before loading'; (3) What it returns: 'Validation status, version, tensor count, and architecture metadata.'
• Replace the generic 'query_payload' parameter with structured inputs: 'header_bytes' (base64-encoded binary), 'metadata' (JSON object), and optional 'architecture' (string enum: llama, mistral, etc.). Add descriptions and constraints for each.
• Document the output schema explicitly in the tool definition, including field types and descriptions: 'valid' (boolean), 'magic' (string), 'version' (integer), 'tensor_count' (integer), 'metadata_kv_count' (integer), 'is_ready_for_mmap' (boolean).
• Integrate the GGUFTensorMetadataHeaderValidator logic into the tool handler. Parse 'header_bytes' and 'metadata' parameters, call validate_header_bytes() and verify_architecture_metadata(), and return the actual validation results instead of echoing the input.