Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context for LLM selection. Parameter 'query_payload' is a generic string with no format guidance, constraints, or examples of valid payloads. No output schema is documented, the tool returns a hardcoded JSON string with 'status' and 'message' fields, but this structure is not declared in the tool definition. No error handling guidance, no idempotency guarantees, and no indication of what 'deterministic, zero-dependency' operations actually entails. The tool appears to be a stub that always returns success, raising questions about actual functionality.

• Document the output schema explicitly in the tool definition. Declare the response structure: {type: 'object', properties: {status: {type: 'string', enum: ['SUCCESS', 'FAILURE', ...]}, message: {type: 'string'}, ...}, required: [...]}}.
• Replace 'query_payload' with a more specific parameter name and add format constraints. If it accepts natural language queries, say so: 'Natural language query describing the CLI command to search for (e.g., "find all docker containers using more than 2GB memory")'. If it accepts structured JSON, document the schema.
• Rewrite the tool description to be concrete and actionable: 'Search terminal session history using natural language queries and return suggested CLI commands with execution safety grades. Use this to find previously-executed commands matching a user intent without manual history browsing.'
• Add error handling guidance: document what errors can occur (invalid query format, no matching commands, service unavailable), whether they are retryable, and what the LLM should do next.
• Rename the tool to reflect its actual purpose. If it searches terminal history, use 'search_terminal_history' or 'search_cli_history'. If it executes commands, use 'execute_cli_command'. The current name is too generic.