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search_db_objects

Search database objects like schemas, tables, columns, and procedures on SQL Server using SQL LIKE patterns to locate specific elements within your database structure.

Instructions

Search database objects (schemas, tables, columns, procedures) on 'sqlserver_local'. Supports SQL LIKE patterns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_typeNoType of database object to search for
patternNoSearch pattern (SQL LIKE syntax, default: '%' for all)
schemaNoFilter by schema name
tableNoFilter by table name (for columns)

Implementation Reference

  • Stub handler for the search_db_objects tool that returns a placeholder response indicating implementation is pending database connector integration.
    },
    async (args) => {
      // TODO: Implement actual database object search
      return createToolSuccessResponse({
        message: `Database object search on '${sourceId}' not yet implemented`,
        filter: args,
        source_id: sourceId,
        note: "Database connector integration pending",
      });
  • Zod schema defining input parameters for the search_db_objects tool: object_type, pattern, schema, table.
    {
      object_type: z.enum(["schema", "table", "column", "procedure", "index"]).optional()
        .describe("Type of database object to search for"),
      pattern: z.string().optional().describe("Search pattern (SQL LIKE syntax, default: '%' for all)"),
      schema: z.string().optional().describe("Filter by schema name"),
      table: z.string().optional().describe("Filter by table name (for columns)"),
  • src/server.ts:135-153 (registration)
    Registration of the search_db_objects tool using McpServer.tool(), including name, description, input schema, and handler. Registered for each database source with optional suffix.
      `search_db_objects${toolSuffix}`,
      `Search database objects (schemas, tables, columns, procedures) on '${sourceId}'. Supports SQL LIKE patterns.`,
      {
        object_type: z.enum(["schema", "table", "column", "procedure", "index"]).optional()
          .describe("Type of database object to search for"),
        pattern: z.string().optional().describe("Search pattern (SQL LIKE syntax, default: '%' for all)"),
        schema: z.string().optional().describe("Filter by schema name"),
        table: z.string().optional().describe("Filter by table name (for columns)"),
      },
      async (args) => {
        // TODO: Implement actual database object search
        return createToolSuccessResponse({
          message: `Database object search on '${sourceId}' not yet implemented`,
          filter: args,
          source_id: sourceId,
          note: "Database connector integration pending",
        });
      }
    );
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the search functionality and SQL LIKE pattern support, but lacks critical behavioral details such as whether this is a read-only operation, potential performance impacts, result limits, error handling, or authentication requirements. For a search tool with no annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose ('Search database objects') and adds essential details (target, pattern support) without waste. Every word earns its place, making it appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is moderately complete for a search tool. It covers the purpose and basic usage but lacks behavioral transparency (e.g., safety, limits) and output details. With 4 parameters fully documented in the schema, it's adequate but has clear gaps in guiding the agent on operational context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'SQL LIKE patterns' (which relates to the 'pattern' parameter) but doesn't provide additional syntax, format details, or usage examples beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Search') and resource ('database objects') with specific examples (schemas, tables, columns, procedures) and target ('sqlserver_local'). It distinguishes from siblings like 'execute_sql' (execution vs. search) and 'get_object' (retrieval vs. search), but doesn't explicitly differentiate from 'search_objects' which might be similar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for searching with SQL LIKE patterns, but provides no explicit guidance on when to use this tool versus alternatives like 'search_objects' or 'get_object_metadata'. It mentions the target database ('sqlserver_local'), which gives some context, but lacks clear when/when-not rules or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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