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 this as read-only and non-destructive, and the description adds meaningful behavioral context: it is free and rate-limited, and the data comes from a weekly universe scan. This goes beyond the structured 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 two short sentences, front-loads the core purpose, and adds only the essential operational detail about rate limits. Every phrase earns its place, and there is no redundant or filler content.
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?
This is a simple one-parameter read-only tool with a defined output schema, so the description does not need to detail return values. It covers the topic scope, data source, and rate-limit constraint. The only notable gap is routing guidance among sibling tools, which was already accounted for in usage guidelines.
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 must carry parameter meaning, but it only restates that the parameter is a research topic. It does not explain valid topic formats, whether exact matches are required, how to discover topics, or any example values. This is inadequate given the schema provides no information.
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 returning a statistical momentum snapshot for one research topic, including rank, growth, and z-scores. It is specific about the resource scope, but it does not explicitly differentiate itself from the sibling tools technology_momentum or technology_emerging, so it falls just short of a 5.
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' implies this tool is for singular topic lookups, which is useful context. However, there is no explicit statement of when to prefer this tool over technology_momentum, technology_emerging, or any other sibling, and no exclusions are provided.
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.