get_pattern_summary

Generate plain English summaries of pattern search results with historical forward returns for retail traders.

Instructions

Generate an AI-written plain English summary of pattern search results.

Returns a concise 2-3 sentence summary suitable for retail traders.

Args:
    query_label: Human-readable query label (e.g. 'AAPL 2024-06-15')
    n_matches: Number of matches found
    horizon_returns: Forward returns dict {1: [...], 3: [...], 5: [...], 10: [...]}

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query_label`	Yes
`n_matches`	Yes
`horizon_returns`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Tool Definition Quality

B3.4/5.0

Behavior3/5

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

Describes output characteristics (AI-written, retail-focused, 2-3 sentences) but lacks operational details like idempotency, side effects, or rate limits given no annotations exist.

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

Conciseness4/5

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

Well-structured with purpose statement, return description, and Args section; appropriately concise with no redundant content.

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

Completeness4/5

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

Adequately complete given the parameter complexity and lack of schema descriptions; covers all inputs and appropriately defers to output schema for return details.

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?

Compensates effectively for 0% schema description coverage by providing semantic meanings and examples for all three parameters (e.g., horizon_returns structure, query_label format).

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

Purpose4/5

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

Clearly states it generates AI-written plain English summaries of pattern search results and distinguishes itself from analysis/search siblings, though could explicitly mention it consumes output from search tools.

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

Usage Guidelines2/5

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

Implies use for summarizing pattern results but provides no explicit guidance on when to use versus analyze_pattern or other siblings, nor when not to use.

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

Install Server

Chart Library