tickerbot_scan

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

With no annotations, the description takes on the transparency burden. It reveals key behavioral constraints: the flat WHERE grammar, no JOIN/subqueries, unlimited asof depth, and the row-vs-aggregate output modes. It does not mention read-only implications, pagination, or error handling, but the stated limits give substantial insight beyond the schema.

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 dense but every sentence earns its place: it opens with the core action, then explains output modes and grammar constraints, and ends with a practical example. No filler or redundancy; it is 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.

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

For a 12-parameter scan tool with no output schema, the description covers the essential conceptual model: row mode, aggregate mode, the query grammar, and a representative example. It does not spell out default row projections or pagination details, but those are sufficiently documented in the input schema, so only a minor gap remains in return-value clarity.

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?

The input schema already describes all parameters in detail (100% coverage), so the baseline is 3. The description adds valuable context by explaining the q grammar (AND/OR/NOT, comparison operators, literals) and demonstrating group_by rollups with examples, which deepens understanding beyond the schema's short parameter descriptions.

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 uses a specific verb+resource ('Run a SQL WHERE clause against the live ticker universe') and clearly distinguishes this tool from siblings by explaining its filtering and aggregation power. It also provides a concrete example, making the purpose unmistakable.

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?

It clearly conveys when to use the tool: for arbitrary SQL-like filtering and aggregations over the ticker universe, with a past-moment option. However, it doesn't explicitly name alternative tools (e.g., list_tickers, get_ticker) or state when NOT to use this in favor of those, so it stops short of full exclusionary guidance.

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