technology_lookupStatistical momentum snapshot of one research topic from the weekly universe scan (rank, growth, z-scores). Free (rate-limited).
| 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 the tool as read-only and non-destructive; the description adds useful behavioral context by noting the data comes from a 'weekly universe scan' and the operation is 'Free (rate-limited)'. It does not contradict 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?
Two short sentences deliver the core purpose, data source, included metrics, and cost/rate-limit warning with no filler. The most important information is front-loaded.
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?
For a single-parameter lookup with an output schema and safety annotations, the description is mostly adequate, but it leaves the topic value semantics under-specified and does not disambiguate from the similarly named sibling tools. An agent could still call it correctly, but may not know how to choose valid topic inputs.
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 0% and the description only says 'one research topic,' adding minimal meaning beyond the parameter name. It does not specify accepted topic formats, whether exact names are required, or how to enumerate valid values.
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?
Description states the tool produces a 'statistical momentum snapshot of one research topic' with concrete fields (rank, growth, z-scores), so the operation and resource are clear. It does not explicitly name sibling tools like technology_momentum to rule out confusion, but the singular 'one research topic' provides a functional distinction.
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 'one research topic from the weekly universe scan' implies this is for a single-topic lookup, but the description never states when to prefer it over technology_momentum or technology_emerging, nor gives exclusions. Usage context is implied rather than explicit.
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.