Three well-named SPARQL tools with clear, action-verb naming (get_, validate_, execute_). Descriptions are substantial and actionable (ranging 126-257 chars, within production baseline of 194±200). All tools have input schemas with proper type definitions. However, output schemas are undocumented (descriptions mention 'toons format' but no structured schema provided), and error handling relies on free-text strings rather than structured error objects with recovery guidance. Tool composition is strong, each has a single responsibility and the chain (get_schema → validate_sparql → execute_sparql) is well-designed for SPARQL workflow. Parameter descriptions are present but could be more prescriptive about constraints (e.g., timeout bounds).

• Document output schema for all tools. For get_schema, define that it returns a single string with markdown sections (Prefixes, Classes, General Tips). For validate_sparql and execute_sparql, define the structure of toons format or provide an example response showing field names, types, and nesting.
• Replace free-text error responses with structured error objects. Return JSON like {"status": "error", "error_code": "SYNTAX_ERROR", "message": "...", "recoverable": true, "suggested_action": "..."} so LLMs can parse and act on errors programmatically.
• Add bounds to the 'timeout' parameter. E.g., 'Optional query timeout in seconds (1-3600, defaults to DPRR_QUERY_TIMEOUT env var, default 600)'.
• Document parameter relationships. The timeout parameter only applies to execute_sparql; make this explicit in parameter descriptions so LLMs understand when it's relevant.
• Consider adding a 'validate_only' boolean parameter to execute_sparql (defaulting to false). When true, the tool runs only the validation steps (already in the code) without hitting the store, enabling explicit dry-run workflows.