Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context for LLM selection. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload for this skill') with no format, constraints, or examples of valid payloads. No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. No error handling guidance, no idempotency markers, and no indication of side effects despite WRITE risk classification. Tool naming is vague ('execute_skill_action') and does not follow verb_noun convention clearly. The implementation is a mock that always returns success, providing no real validation or error scenarios.

• Document the complete output schema with typed fields. Example: {type: 'object', properties: {status: {type: 'string', enum: ['SUCCESS', 'FAILURE', 'TIMEOUT']}, message: {type: 'string'}, execution_id: {type: 'string'}, duration_ms: {type: 'number'}}}
• Expand tool description to 150-250 chars explaining: (1) what autonomous browser operations it performs, (2) when to call it vs other tools, (3) what it returns, (4) any prerequisites (e.g., page must be loaded).
• Add format and constraint documentation to 'query_payload': specify expected structure (JSON object? string? XML?), required fields, max length, and provide a concrete example of a valid payload.
• Implement real error handling: document failure modes (timeout, invalid payload, DOM not found), classify each as retryable/user-fixable/fatal, and return actionable error messages with recovery hints.
• Add tool annotations: mark as destructiveHint=true (since it modifies DOM), idempotentHint=false (if side effects are irreversible), and document confirmation requirements for irreversible operations.