recommended_patterns

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

With no annotations provided, the description carries full responsibility. It discloses a critical non-obvious behavior: hist_win_w20 is an aggregate bucket property, not a probability for the specific signal, and that values ≤0.5 imply no edge. It also reveals free-tier behavior (2 rows, null win-rate, is_billed flag). This is far beyond typical transparency and directly prevents misinterpretation.

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 front-loaded with the core purpose, then provides essential interpretive detail in a separate paragraph, and ends with a clean Args section. It is not overly verbose given the importance of the caveats; each sentence earns its place, though the phrasing could be tightened slightly without losing meaning.

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?

An output schema exists, so the return format is not needed in the description. The description covers what the tool returns (patterns with win-rates), how to interpret the win-rate, the impact of the limit on free accounts, and the logical check (is_billed). Combined with the output schema, an agent has everything needed to invoke it correctly and interpret results safely.

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 0%, and there is only one parameter (limit). The description adds significant meaning: it defines the effective maximum (100), the free-account default behavior (2 rows), and the side effect of null hist_win_w20. This fully compensates for the lack of schema 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 clearly states the resource: K-line chart patterns flagged by the daily scan, with the win-rate of their pattern bucket. It uses a specific subject and modifies it with the key field (hist_win_w20). It is distinct from siblings like market_stretch and deviation_ranking, which focus on other metrics.

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 gives clear context for when to use the tool (for pattern results) and, more importantly, how to interpret the output responsibly (report the number, not a recommendation). It notes the free-tier limitation indirectly via the `limit` parameter and `is_billed` check. However, it does not explicitly mention alternatives like 'when NOT to use this tool', but the scope is so specific that this is not a major gap.

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