technology_momentumDeep on-demand momentum analysis of any technology topic: fresh OpenAlex evidence, growth series, leading institutions/researchers, LLM-scored momentum with grounded confidence and rationale. $0.05/call. Not investment advice.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only, but the description adds substantial behavioral context: it uses fresh OpenAlex evidence, produces LLM-scored momentum with grounded confidence and rationale, has a $0.05/call cost, and explicitly disclaims investment advice. This is strong disclosure beyond the structured 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?
One tight, front-loaded sentence that captures the core purpose, expected outputs, pricing, and an important caveat. Every clause earns its place and there is no repetition of schema or annotation information.
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 single parameter, existing output schema, and annotations, the description covers what the tool does, what evidence it uses, what outputs to expect, the cost, and the non-investment-advice limitation. Nothing essential is missing for an agent to invoke it 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?
With 0% schema description coverage, the description carries the full burden for the single 'topic' parameter. 'Any technology topic' clearly conveys that the topic is free-form and not restricted to a predefined list. It could have added an example, but the semantics are adequately communicated for a single simple parameter.
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?
States a specific verb ('analysis') and resource ('any technology topic') and lists concrete outputs: OpenAlex evidence, growth series, leading institutions/researchers, LLM-scored momentum. It is clearly about momentum rather than lookup or emerging signals, but does not explicitly distinguish itself from sibling tools such as technology_lookup or technology_emerging.
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?
'Deep on-demand momentum analysis of any technology topic' implies a broad use case and signals that this is a general-purpose analysis tool. However, it does not state when to prefer this over sibling options or mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.