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 establish readOnlyHint=true and openWorldHint=true. The description adds useful behavioral context: it mentions fresh OpenAlex evidence, LLM-scored momentum with grounded confidence and rationale, and a cost of $0.05/call, plus a 'not investment advice' disclaimer. This goes beyond the annotations without contradicting them.
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 compact and front-loaded with the core purpose, followed by specific output details, pricing, and disclaimer. Every sentence adds valuable 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?
With an output schema present, return value details are already covered structurally. The description supplies the question the tool answers, the key data sources, the analytical approach, pricing, and a limitation. It could name sibling tools for clearer differentiation, but for a one-parameter analysis tool this is largely complete.
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 only defines 'topic' as a string with no description (0% coverage). The description compensates by clarifying that the topic can be 'any technology topic', which adds meaningful semantic scope. It does not give examples or formatting guidance, but for a single free-text parameter this is reasonably sufficient.
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 identifies the tool as on-demand momentum analysis for any technology topic, and elaborates with concrete outputs like fresh OpenAlex evidence, growth series, leading institutions/researchers, and LLM-scored momentum. It does not explicitly contrast itself with sibling tools like technology_emerging or technology_lookup, but the core purpose is unmistakable.
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 phrase 'Deep on-demand momentum analysis of any technology topic' gives a clear context for when to use this tool: when a broad, current momentum assessment of a technology is needed. It does not provide exclusions or explicit alternative routing to siblings, but the usage context is strong enough.
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.
Each tool targets a distinct data domain: billing/product info, federal funding, federal regulations, SEC filings/events, and research topic analytics. The three technology_* tools are related but differentiated by scope (ranked list, snapshot, deep analysis), so an agent should be able to select correctly with descriptions. Minor potential confusion exists between funding_signals and sec_events since both emit 'signals', but they cover clearly different sources.
All names are snake_case and map to clear domains, but the pattern is not uniform: list_products uses verb_noun, while funding_signals, reg_rules, sec_company, and technology_lookup use noun-style names. The sec_* and technology_* prefixes help navigation, but there is no consistent verb convention across the set.
Nine tools is a well-scoped size for a data-as-a-service server. It includes meta tools (credit, product list) and seven data products without obvious redundancy. Each tool appears to earn its place.
As a read-only data API, full CRUD is not expected, and the server covers billing, product selection, federal money, regulations, SEC events/company snapshots, and emerging tech analytics. The main gap is a lack of a general search/discovery tool, but the specialized endpoints cover their stated purposes well.