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

Annotations already declare readOnlyHint=true. The description adds meaningful behavioral details: ranking by embedding cosine similarity, k bounds, the at-least-one-input constraint, and the 'Analytical, not advice' disclaimer. No contradiction with annotations.

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?

Description is dense but not bloated; it front-loads the core purpose and packs constraints, REST mirror, and a caveat into a short space. Slightly informal tags ('DRILL-DOWN', 'Pro') add color without harming clarity.

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 3-param read-only search tool with no output schema, the description covers inputs, output ranking, constraints, and context. It doesn't specify return fields, but that's not required given the simplicity; the overall picture is complete enough for an agent to select and invoke it.

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 compensates by explaining k's range/default, q as free-text query, and coin as a filter, plus the constraint to provide at least one of coin/q. It does not give examples of coin format, but the semantics are clear.

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?

Clearly states it performs semantic similarity search across the signal corpus using a coin and/or free-text query, returning k most similar past signals ranked by embedding cosine similarity. This distinguishes it from sibling tools like list_signals or get_signal_with_context by emphasizing the similarity-search nature.

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?

Provides clear context for use ('DRILL-DOWN — history rhymes', 'have we seen this setup before') and input requirements ('Provide at least one of coin / q'). However, it doesn't explicitly compare with sibling tools like get_market_analogs, so it stops short of full when/when-not guidance.

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