smart-query

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the core function (understanding and translating natural language to queries) but lacks details on how it handles ambiguous questions, error conditions, performance characteristics (e.g., response time), or output format. For a tool with no annotations, this is a significant gap in transparency.

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 a single, clear sentence in Turkish that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core functionality, making it easy to understand quickly. Every part of the sentence earns its place by defining the action and input.

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?

Given the complexity of natural language processing and database query translation, the description is incomplete. It lacks details on output (no output schema provided), error handling, or behavioral traits, and with no annotations, it fails to provide sufficient context for effective use. This is inadequate for a tool that likely involves nuanced processing.

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 description coverage is 100%, so the schema already documents both parameters ('language' and 'question') with descriptions and enum values. The description adds minimal value beyond the schema by implying the 'question' parameter is in natural language, but does not provide additional context such as supported question types or limitations. Baseline 3 is appropriate as the schema does the heavy lifting.

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's purpose: 'Doğal dilde sorulan soruyu anla ve uygun veritabanı sorgusuna çevir' (Understand a question asked in natural language and translate it to an appropriate database query). This specifies the verb (understand and translate) and resource (natural language question to database query), but does not distinguish it from sibling tools like 'analyze-database' or 'get-table-info', which might involve database interactions but are not explicitly about natural language translation.

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 no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or specific contexts for usage, such as when to prefer this over direct query tools or other analysis tools in the sibling list. This leaves the agent without clear direction on application scenarios.

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