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yugui923

db-connect-mcp

by yugui923

search_objects

Search and explore database objects like schemas, tables, columns using SQL LIKE patterns. Choose from three detail levels to balance token usage with metadata completeness.

Instructions

Search and explore database objects (schemas, tables, views, columns, indexes) with progressive disclosure for token efficiency. Use SQL LIKE pattern (% matches any sequence, _ matches one character) to match names. Three detail levels: 'names' (most token-efficient), 'summary' (default, key metadata), 'full' (includes comments and full type info). For column/index search, narrow with schema (and optionally table) to avoid the per-call table cap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
patternYesSQL LIKE pattern to match object names. Use '%' to match all, '%user%' for substring, '_d' for single-char wildcard.
object_typesNoTypes of objects to search. Defaults to all 5 types. Restricting types is faster.
detail_levelNoResponse verbosity. 'names' returns just identifiers (cheapest), 'summary' adds key metadata, 'full' includes comments and type details.summary
schemaNoRestrict search to a specific schema. Strongly recommended when searching columns or indexes.
tableNoRestrict column/index search to a specific table. Without `schema`, matches a table of that name in any schema.
limitNoMax items to return (1-1000). Total match count is reported separately in 'total_found'.
Behavior4/5

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

Discloses progressive disclosure levels, SQL LIKE pattern matching, and per-call table cap. As a search tool, it is read-only, and no annotations exist, so description covers key behaviors well.

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?

Three sentences with front-loaded purpose, no unnecessary words. Each sentence adds essential information.

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

Completeness5/5

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

Despite 6 parameters and no output schema, the description covers all parameters, use cases, and behavioral constraints comprehensively. No gaps identified.

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

Parameters4/5

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

Schema coverage is 100%, so baseline 3. Description adds value by explaining progressive disclosure, narrowing strategies, and detail levels, exceeding schema descriptions.

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

Purpose5/5

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

Clearly states verb 'Search and explore', resource 'database objects', and distinct features like progressive disclosure and SQL LIKE patterns. Distinguishes from sibling tools like list_schemas or describe_table.

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

Usage Guidelines4/5

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

Provides clear guidance on narrowing column/index search with schema and table parameters to avoid a per-call cap. However, does not explicitly mention when not to use or compare to siblings.

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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