cos_learn_validate

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

Beyond the annotations (which only indicate readOnlyHint=false), the description details the behavioral mechanics: it updates confidence using 'brain-inspired formulas' with specifics like LTP diminishing returns and LTD proportional penalties. It also discloses the return value (JSON with old/new confidence and validation status). This adds rich context beyond what annotations provide.

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 well-structured and concise: a single-sentence purpose, a compact bullet list for formulas, followed by Args and Returns sections. Every sentence earns its place, and the main action is front-loaded. It is neither bloated nor under-specified.

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 this is a simple two-parameter tool with an output schema (has output schema: true), the description is complete. It covers purpose, behavioral nuance, parameter semantics, and return format. There is no missing critical information for an agent to invoke it correctly.

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%, so the description fully carries parameter meaning. It explains pattern_id as 'ID in learned_patterns table' and was_helpful as 'Whether the pattern was useful (default True).' This is precise and adds meaning beyond the bare type/default in the schema.

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 opens with a clear, specific action: 'Record whether a suggested pattern was helpful.' This distinct verb-resource pair unambiguously states the tool's function and distinguishes it from siblings like cos_learn_suggest (which suggests) and cos_learn_extract (which extracts).

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 context is clearly implied: this is for providing feedback on a suggested pattern, whether it was helpful or not. However, it does not explicitly name alternatives (e.g., 'use cos_learn_suggest to generate suggestions') or state when not to use it. This is clear context without explicit exclusions, so a 4 is appropriate.

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