This server exposes two dynamically-generated tools that lack explicit, verifiable schema definitions and clear separation of concerns. Tool 1 (dynamic_mcp_tools) is a meta-tool that wraps external MCP server tools, its actual parameters and output schema are not defined in this codebase, making it impossible to score the individual tool quality. Tool 2 (a2a_skill_tools) has a minimal schema with only a 'prompt' parameter and a generic string description. Neither tool has documented output schemas, error handling guidance, or parameter validation rules. The code shows attempt at schema conversion and parameter building (lines in mcp_client/agent_tools.py), but the actual tool registration is incomplete: tools are inferred from external servers rather than explicitly defined. This violates the principle that tool definitions must be self-contained and verifiable. The descriptions are too generic to guide LLM tool selection ('Dynamic tools fetched...' and 'Skills from A2A servers...' do not explain WHEN or WHY to use them). No evidence of security checks, permission gates, or audit logging beyond basic Python logging.

• Refactor tool registration: Instead of dynamically wrapping external MCP tools, define explicit tool stubs in this server with full schemas, descriptions, and output specifications. Use a static registry or manifest to declare which external tools are available.
• Add verb-driven action names: Rename 'dynamic_mcp_tools' to something like 'invoke_external_tool' or 'run_mcp_command', and 'a2a_skill_tools' to 'execute_skill' or 'run_skill'. Ensure each name starts with an action verb.
• Document output schemas: For each tool, add a schema documenting the response structure, field types, and required fields. Include examples (but do not use them as the source of truth, use formal schema constraints).
• Expand tool descriptions: Each description should answer: What does this tool do? When should I call it instead of another tool? What does it return? Example: 'Invokes an external MCP server tool by name. Use this to access tools from integrated services (weather, data lookup, etc.). Returns the tool's result as a JSON object or error message.'
• Add parameter constraints: For the 'prompt' parameter in a2a_skill_tools, specify min/max length, character restrictions, and examples of valid prompts. Use an enum if prompts are limited to a known set.
• Implement error recovery guidance: When a tool call fails, return a structured error object with (1) error code, (2) human-readable message, (3) suggestion for recovery (retry, try another tool, ask user). Example: 'Tool not found. Available tools: [list]. Did you mean: X?'