pricing_infoFree — list every paid tool in the 'us-brand-intelligence' bundle with its price, payTo address and network. Call this first if you don't have a wallet ready yet.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive behavior. The description adds that the call is 'Free' and advises calling first when no wallet is ready, adding context beyond annotations. No contradiction with 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 brief sentences. The first is front-loaded with 'Free — list...' followed by specifics; the second adds usage guidance. No wasted words.
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 low complexity (no params, no output schema, read-only), the description sufficiently explains what the tool does and when to call it. It mentions the key output fields (price, payTo, network).
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?
Tool has zero parameters, so schema is empty. Description doesn't need to explain params; baseline is 4. It clearly implies there are no inputs needed by saying 'list every...' without conditions.
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 clearly states the verb 'list' and specific resource 'every paid tool in the us-brand-intelligence bundle' with details on price, payTo address, and network. It distinguishes this tool from all sibling tools, which focus on signals and evidence, not pricing.
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?
Provides explicit guidance to 'Call this first if you don't have a wallet ready yet', indicating a clear use case. It doesn't explicitly name alternative tools, but the context implies when to use it preferentially.
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.
Many tools have overlapping purposes, e.g., us-brand-signal-metrics-aggregator, us-brand-signal-cross-tabulator, and us-brand-signal-brand-summary all produce counts or summaries of the same type of data. The descriptions are detailed, but the boundaries between analyzers, summarizers, and aggregators are unclear enough that an agent may struggle to pick the right one.
Most tools follow a consistent 'us-brand-' prefix with descriptive nouns separated by hyphens (e.g., us-brand-signal-batch-splitter, us-brand-evidence-chronology-builder). The exception is 'pricing_info', which uses an underscore and lacks the prefix, creating a minor inconsistency.
At 21 tools, the set is on the heavier side but still manageable for a complex domain like brand intelligence. Many tools are variations on deterministic signal processing, which could be streamlined, but the count itself is not extreme.
The tool set covers a broad pipeline for processing buyer-supplied signals and evidence, including splitting, summarizing, routing, and building payloads. However, it lacks any tools for ingesting or fetching data from external sources (except one federal award snapshot), and there is no end-to-end controller that orchestrates the workflow. This leaves notable gaps for a complete 'brand intelligence' lifecycle.