Single tool with critical definition gaps. Tool name 'quantize_vector' lacks action verb prefix (should be 'compress_vector' or 'quantize_to_uint8'). Description is minimal (8 words, well below 20-char threshold). Input schema is severely incomplete: 'vector' parameter has no type specification beyond 'array', no item type, no description, no constraints. Output schema is entirely undocumented, LLM cannot predict return structure. No error handling guidance. No parameter validation rules. Tool is READ_ONLY but this is not reflected in descriptions or error patterns.

• Expand tool description to 100-150 chars: 'Compress a float32 vector to uint8 (0-255) representation for 4x RAM reduction. Returns quantized bytes plus min/max calibration factors for reconstruction. Use before storing embeddings or computing asymmetric similarity.'
• Add full input schema with item type: {"type":"object", "properties": {"vector": {"type":"array", "items": {"type":"number"}, "description": "Float32 vector to quantize. Length 1-10000. NaN/Inf values will be clamped to min/max range.", "minItems": 1}}, "required": ["vector"]}
• Document output schema in tool description or separate schema field: Returns {uint8_vector: [0-255], min_val: float, max_val: float, dimensions: int}. Include this in the response wrapper so LLM can chain to dequantize_vector or asymmetric_dot_product.
• Rename tool to 'compress_vector_to_uint8' to start with action verb and clarify it's a compression operation, not a generic quantization.
• Add error handling guidance: 'Empty vector returns {uint8_vector: [], min_val: 0.0, max_val: 0.0}. Non-numeric values will raise ValueError with the offending index.'