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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real value by disclosing that the corpus is an internally authored, curated framework rather than retrieved web content, and that returns are 'short passages ... with relevance scores'.

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 what the tool is, followed by what it returns. No filler or repetition of the title.

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?

With an output schema present, return values need not be explained, and the description covers purpose, usage context, and return shape adequately. Only the absence of explicit when-not guidance keeps it from a 5.

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 100%, so both 'query' and 'top_k' are already documented in the schema; the baseline of 3 applies. The description adds no syntax, example, or default guidance beyond what the schema provides.

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?

States a specific verb ('look up') and a precisely scoped resource: MarketCrew's distilled, synthesized TA methodology notes, explicitly contrasted with raw data by the phrase 'our own synthesized notes.' An agent can distinguish this from get_technical_indicators or public_search_news without opening a schema.

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?

It gives an implied use case ('to ground an answer in a consistent framework') but never states when to prefer this over siblings such as get_technical_indicators or when it is inappropriate. Usage is inferable but not explicit, and no alternative is named.

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