Analyse a technology or research field

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

Annotations already declare read-only, non-destructive, idempotent and open-world, so the bar is lower, yet the description adds genuinely new behavior: it runs the external Apify Actor tacps126/innovation-radar on the caller's account, costs ~$0.03 per field, and caps spend at $0.5 per call. It also discloses that multiple fields are returned ranked, which affects how results should be interpreted.

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 purpose is front-loaded and the long enumerative sentence is dense but purposeful, since each listed output helps an agent decide to call it. The trailing two sentences (alternate prompt, external Actor and cost) also earn their place, though the output enumeration is longer than strictly necessary given an output schema exists.

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 an output schema exists, return values need not be explained in detail, yet the description still signals the shape of results. Combined with annotations covering safety and the description covering external dependency and cost, an agent has everything needed to call correctly.

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 the schema already carries parameter meaning and baseline is 3. The description adds batching semantics ('Several fields come back ranked') and cost-per-field implications that clarify why 'fields' is an array and how 'years' scope the trend, giving a modest lift above baseline.

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 opens with a concrete verb+resource ('A landscape of a technology or research field from open research data') and enumerates the specific outputs (opportunity score, momentum, lab-to-market gap, works per year, top companies, key researchers, patents). This is clearly distinguishable from siblings like search_research (raw retrieval) or validate_idea.

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 a clear usage context (analysing/ranking whole fields) and explicitly routes the agent elsewhere for a related need: 'For written opportunities, use the field-opportunity-analysis prompt.' It lacks explicit exclusions versus sibling tools such as search_research or validate_idea, so it stops short of a 5.

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