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

Annotations already declare readOnlyHint and idempotentHint, and the description adds useful behavioral context by clarifying that it returns short synthesized passages with relevance scores rather than raw data. This goes beyond the schema and annotations without contradicting them.

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?

Two sentences, front-loaded with the main purpose. Every clause earns its place—the em-dash notes clarify the nature of the content, and the second sentence discloses the return format. No fluff.

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 simple, read-only retrieval tool with an output schema and full parameter docs, the description adequately covers purpose, return shape, and applicability. There is no missing behavioral or contextual information needed for correct invocation.

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 has 100% description coverage for both parameters (query and top_k), including an example in the schema. The description's mention of 'what you want the methodology on' adds no new meaning beyond the schema, so the baseline of 3 is appropriate.

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 ('Look up') and names the resource ('MarketCrew's distilled technical-analysis methodology') with qualifiers that distinguish it from sibling data tools like get_technical_indicators or get_key_levels. It clearly communicates this is methodology/notes, not raw data.

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 phrase 'to ground an answer in a consistent framework' gives a clear situational context for when to use this tool. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a full 5.

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