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

With no annotations, the description carries the burden of behavioral disclosure. It explains the underlying algorithm (pgvector, HNSW cosine similarity) and return format, but it does not disclose edge-case behavior, error handling, or side effects. Since this is a read-only lookup, it is acceptable but not fully transparent.

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 with clear sections for purpose, technical details, usage scenarios, return fields, and an example. Every sentence adds value, and the key functionality is front-loaded in the first line.

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?

The description is quite complete for a read tool with 5 parameters and no output schema. It explains the return structure, similarity semantics, and gives a usage example. It lacks detail on filtering behavior for country/venue_type, but the schema descriptions cover that.

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 already covers 100% of parameters with descriptions, so the baseline is 3. The description adds an example that demonstrates a typical invocation, but it does not enrich parameter semantics 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?

The description clearly states the tool's function: finding screens with similar audience profiles using pgvector similarity. It distinguishes itself from sibling tools like semantic_audience_search by focusing on lookalike audience matching for campaign expansion and targeting.

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?

A dedicated 'WHEN TO USE' section provides clear scenarios (expanding reach, finding similar inventory, building lookalike segments). It does not explicitly mention when not to use or name alternative tools, but the guidance is concrete and actionable.

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