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

Beyond the annotations, the description discloses meaningful traits: the corpus size, continuous curation, confidence grading, source traceability, and Pro/Enterprise-only access to full_text and source. It does not cover rate limits, auth requirements, or pagination behavior, so it is strong but not exhaustive.

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 INPUT, RETURNS, and USE WHEN sections, and the purpose is front-loaded. It is somewhat verbose and duplicates several schema and return-schema details, which slightly weakens conciseness.

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, rich schema, output schema, and annotations, the description provides everything an agent needs: what is searched, required and optional inputs, ranked return shape, confidence semantics, tier gating, and the intended use case.

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 coverage is 100%, and the description largely repeats the schema details for query, top_k, category, and min_confidence rather than adding new semantics. The practical guidance about raising min_confidence is already present in the schema description, 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 states a specific verb and resource: semantic search over the Proximens GEO Engine knowledge base. It clearly scopes the tool to 8,000+ GEO/AEO principles with confidence scores and sources, making it easy to distinguish from lookup-oriented siblings like get_principle or list_categories.

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 'USE WHEN' clause gives a clear context for the tool: evidence-backed answers about how AI search engines select, rank, and cite content. It does not explicitly state when not to use it or name alternatives such as bulk_search or get_principle, so it falls 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.