This server has 14 tools with basic schemas and descriptions, but significant quality gaps. Most tool descriptions are generic (10-50 chars), well below the 194-char baseline for production tools. Parameter descriptions exist but are minimal. No output schemas documented. Error handling is absent from visible code. Tool names follow verb_noun convention (get_*, execute_*, add_*), which is good, but descriptions lack context on WHEN to use each tool, prerequisites, or what the LLM should expect. The execute_nonquery and execute_routine tools accept free-form SQL/parameters without validation guidance, creating injection risks. No evidence of pagination, result limits, or structured output shaping. The server appears to be a generic database abstraction layer rather than a domain-specific tool suite optimized for agent reasoning.

• Expand all tool descriptions to 100-200 chars. Include WHEN to use (e.g., 'Call get_tables first to discover available tables before querying'), prerequisites, and expected output structure.
• Document output schemas for all tools. Specify field names, types, and whether results are paginated. E.g., get_tables should return {tables: [{name: string, schema: string, rowCount: int}], total: int}.
• Add result limits and pagination. Cap get_tables, get_columns, execute_reader at 50 items by default; accept limit and offset/cursor parameters. Document the cap in descriptions.
• Add validation and error guidance to execute_nonquery and execute_routine. Describe allowed SQL operations, parameter format (e.g., 'JSON object with param names as keys'), and return actionable errors like 'Invalid SQL: only SELECT, INSERT, UPDATE, DELETE allowed' or 'Parameter mismatch: expected {id: int, name: string}, got {id: string}'.
• Add parameter descriptions for all inputs. E.g., schema param: 'Database schema name (case-sensitive, e.g., dbo, public). Required.' tableName: 'Table name within the schema (case-sensitive). Required.'