This server presents a single dynamically-generated tool with severely limited documentation and schema definition. The tool 'dynamic_tools_from_predictions' lacks concrete parameter descriptions, proper schema structure, and meaningful guidance for LLM selection. The description is vague about what the tool actually does, it references 'AI action predictions' and 'PredictionLoader' without explaining the actual behavior users/LLMs should expect. The input parameter 'provideAllValuesInPlainEnglish' is poorly named (not verb_noun convention) and lacks clarity on what format, constraints, or expected values are valid. No output schema is documented. The codebase shows a Spring Boot MCP implementation, but the tool definition appears to be dynamically constructed at runtime via 'PredictionLoader', making it impossible to validate the actual schema contract statically.

• Explicitly define and register all tool schemas in code, not dynamically at runtime. Each tool must have a static name, description, input schema, and output schema.
• Rename the tool from 'dynamic_tools_from_predictions' to a verb_noun form that describes an actual action, e.g., 'execute_action', 'invoke_prediction', or 'run_java_method'. The new name should be clear without reading the description.
• Expand the tool description to at least 50 - 150 characters and include: (1) What the tool does in concrete terms, (2) When the LLM should call it, (3) What it returns, (4) Any prerequisites or prerequisites actions.
• Replace the parameter 'provideAllValuesInPlainEnglish' with a clear name like 'parameters' or 'action_parameters', add a type (object? string?), and provide a detailed description of the expected structure and format.
• Document the complete output schema with field names, types, and descriptions. If the tool returns a result, action_id, success flag, or error message, specify all of them.
• Add parameter constraints: if the tool accepts enum values (e.g., action types), declare them as an enum. If there are length limits, specify them.
• Implement explicit error handling with recovery guidance. Example: 'If prediction fails, try with fewer parameters' or 'If action is not found, call list_available_actions first.'
• Consider splitting dynamic tool registration into a discovery tool (e.g., 'list_available_actions') that returns available predictions, and an execution tool (e.g., 'execute_action') that takes an action name and parameters. This follows the single-responsibility pattern.