list_domains
List Cassandra prediction verticals available via predict(domain, entity), with what each predicts and its data sources.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
cassandra-predict
TDQS
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states that the tool lists verticals and includes what each predicts and its data sources, making the read-only nature and output content transparent. It does not describe output format, but for a simple listing tool, this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that conveys the tool's purpose and output scope without unnecessary words. It is efficient and earns a perfect score for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter listing tool with no output schema, the description is complete. It explains what the list contains (vertical names, what each predicts, and data sources) and how it relates to predict, giving an AI agent all necessary context to invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so there are no parameter semantics to explain. The description appropriately says nothing about parameters, and the baseline for zero-parameter tools is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and clearly identifies the resource: Cassandra prediction verticals. It further explains that these verticals are available via predict(domain, entity), which distinguishes it from the sibling tool predict by focusing on enumeration rather than prediction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is used to discover prediction verticals and their details before calling predict. It does not explicitly state 'use this when you need to see available domains,' but the relationship to predict is clear, providing sufficient context for when to use this tool over its sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
A4.5/5.0
Disambiguation5/5
The two tools have clearly distinct purposes: list_domains enumerates the available prediction verticals, while predict executes a prediction for a given domain and entity. There is no overlap or ambiguity between them.
Naming Consistency4/5
The naming is mostly consistent: list_domains follows the verb_noun pattern, while predict is a simple verb. The slight difference in style is minor and both names are intuitive.
Tool Count4/5
With only 2 tools, the server is minimal but appropriately scoped for a focused prediction API. The list_domains helper complements the core predict action without unnecessary bloat.
Completeness4/5
The tool set covers the essential workflow: discovering available domains and making predictions. Minor gaps exist (e.g., no way to fetch historical predictions or get entity details), but they are not critical for the stated purpose.