technology_emergingRanked list of emerging research topics from a weekly statistical scan of all ~4,500 OpenAlex topics (growth, acceleration, citation quality). $0.05/call.
| Name | Required | Description | Default |
|---|---|---|---|
| field | No | ||
| limit | No | ||
| min_works | No | ||
| emerging_only | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, non-destructive behavior. The description adds useful context beyond those: it is a weekly batch scan over roughly 4,500 topics, and it explicitly notes cost ($0.05/call). It does not detail pagination or response shape, but an output schema exists to cover return structure.
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 one dense sentence that front-loads the core output, scope, methodology, and cost. Every clause adds useful information with no filler or repetition.
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?
Although the output schema exists and annotations cover safety, the definition is incomplete for safe invocation: parameter semantics are entirely absent, and sibling-tool differentiation is missing. An agent could call this tool, but it would be guessing at parameter meaning and might select this tool over technology_momentum incorrectly.
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 0%, and the description provides no explanation of field, limit, min_works, or emerging_only. With no parameter documentation anywhere, the agent cannot know valid field values, default behaviors beyond schema defaults, or how emerging_only changes results. The description fails to compensate for the total lack of parameter semantics.
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 deliverable: a ranked list of emerging research topics from a weekly statistical scan of OpenAlex topics, with selection criteria named (growth, acceleration, citation quality). This is clear and indicates provenance, though it does not explicitly contrast with sibling tools like technology_momentum or technology_lookup.
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?
There is no guidance on when to use this tool versus alternatives. Given siblings such as technology_momentum, technology_lookup, and funding_signals, the description leaves the agent to infer selection criteria on its own. No exclusions or preferred use cases are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most tools are clearly separated by domain or action (credit, products, regulations, SEC, technology). The three technology tools could be confused at first glance, but their descriptions differentiate a ranked scan, a single-topic snapshot, and an on-demand deep analysis well.
Tool names follow a predictable lowercase snake_case pattern using noun phrases like credit_balance, funding_signals, and sec_events. The single deviation is list_products, which uses a verb_noun form, but the overall style remains consistent and readable.
Nine tools is well-scoped for a multi-domain intelligence API covering account balance, product catalog, funding, regulations, SEC data, and technology research. Each tool represents a distinct product offering without unnecessary sprawl.
The tool set covers the advertised product surface well, including account management, product discovery, and four data verticals. Minor gaps exist, such as no bulk funding search or regulatory detail drill-down beyond the provided signals, but agents can accomplish core workflows without dead ends.