db_search

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

No annotations are provided, so the description carries full burden. It discloses a key behavioral trait (searches a local snapshot, no DB connection), but does not mention other details like search accuracy or potential limitations.

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

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

The description is concise with two sentences and examples. It is front-loaded: first sentence states the purpose, second adds a key constraint. The examples are helpful but could be integrated more smoothly.

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

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

The description covers core functionality and the offline nature, but it does not describe the output format (e.g., list of tables/columns) despite having no output schema. Completeness is adequate for a simple search tool but not exceptional.

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

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

Schema coverage is 100% with parameter descriptions. The tool description reinforces the query parameter with concrete examples (e.g., 'tables related to authentication'), adding meaning beyond the schema. The limit parameter is adequately covered.

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

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

The description clearly states the tool searches the database schema by natural language query to find tables and columns matching a concept. It includes concrete examples and distinguishes itself from siblings by noting it uses a local snapshot without a DB connection.

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

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

The description provides clear context for when to use the tool (for concept-based schema search) and an important constraint (no DB connection), but it does not explicitly state when not to use it or compare with siblings like db_schema or search_code.

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