Hydra has 15 tools with significant definition gaps. Most tool descriptions are present but lack depth, many are under 100 chars and do not explain WHEN to use the tool or what it returns. Parameter descriptions exist but are minimal (e.g., 'Name of the tool to run'). No input schemas are visible in the source code provided; tool definitions appear to be inferred from function signatures rather than explicitly registered with JSON Schema. Critical issue: tools like run_hydra_tool and run_tool accept 'arguments' as a JSON string, forcing the LLM to serialize/deserialize manually, error-prone and violates the structured input pattern. No output schemas documented. Error handling is absent, no guidance on recovery, retryability, or what to do on failure. Seed tools (currency_convert, fetch_webpage, etc.) are better-described than meta-tools, but still lack actionable error messages and output structure documentation.

• Explicitly register all tools with full JSON Schema input definitions in the MCP server code. Use fastmcp's @server.tool() decorator with complete schema objects, not inferred signatures.
• Replace 'arguments' string parameters with typed object parameters. E.g., run_tool(tool_name: str, **kwargs) where kwargs are the actual tool parameters, or accept a structured dict with type hints.
• Document output schemas for all tools. E.g., list_tools returns {tools: [{name: str, description: str, parameters: dict}]}. Include pagination info (total_count, next_cursor) for list tools.
• Expand meta-tool descriptions to 150 - 250 chars. Explain the distinction between list_hydra_tools (Hydra-generated tools) vs list_tools (all available tools). Add error guidance: 'If reload_tools fails, check that tool files are valid Python.'
• Add confirmation step to remove_tool: return {status: 'confirm_required', tool_name: str, message: 'Are you sure?'} and require a second call with confirm=true. Or implement dry-run mode.