instinct

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: case-insensitive substring match, SQLite FTS backing for speed, multi-word queries with OR semantics, and return ordering by confidence. However, it doesn't mention rate limits, authentication needs, or error handling, leaving some gaps.

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 appropriately sized. It front-loads the purpose, follows with usage guidelines, then provides detailed parameter and return information. Every sentence earns its place with no wasted words, and the formatting (paragraphs, bullet-like sections) enhances readability.

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 tool's complexity (search across multiple fields with FTS), no annotations, and an output schema (which covers return values), the description is complete. It explains the search behavior, when to use it, parameter details, and references the output structure, providing all necessary context for an AI agent.

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?

With 0% schema description coverage, the description fully compensates by providing rich parameter semantics. It explains the 'query' parameter in detail: 'Search term. Can be a partial pattern key, a metadata value, or explain text. Multi-word queries match any word (OR semantics).' This adds significant value beyond the minimal 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 clearly states the tool's purpose: 'Find patterns whose key, metadata, or explain text contains the query.' It specifies the verb ('Find'), resource ('patterns'), and scope ('key, metadata, or explain text'), and distinguishes it from siblings like get_instinct() and list_instincts().

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 explicit guidance: 'Use this when you remember roughly what a pattern was about but not the exact key.' It also specifies when to use alternatives: 'For exact-key lookup use get_instinct(). For browsing by category or project without a keyword, use list_instincts().' This clearly defines the tool's context and exclusions.

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