list_quality_policiesList the immutable quality policies for a given study ID to understand the data validation rules and constraints applied to your marketing mix model.
Read immutable quality policies for the study.
| Name | Required | Description | Default |
|---|---|---|---|
| study_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the domain fact that quality policies are 'immutable', which is useful, but it does not explain return behavior, ordering, or how policies come to exist.
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 one short sentence with no wasted words. The verb 'Read' is front-loaded, and the resource and scope are stated immediately.
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 simple read-only tool with one required parameter, an output schema, and safety annotations, the description is nearly complete. It identifies the resource, the scope, and the immutability of the returned policies. The only real gap is that it does not point the agent to `create_quality_policy` for creating policies, but that is a usage-guidance concern rather than a critical invocation blocker.
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 description coverage is 0%, so the description must compensate. It maps the action to 'the study', which corresponds to `study_id`, but does not explain where the ID comes from or how it is validated. The single parameter is self-evidently the study identifier, so minimal compensation is acceptable.
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 a specific verb ('Read'), resource ('quality policies'), and scope ('for the study'). It is immediately distinguishable from the sibling `create_quality_policy` by virtue of the read/creation contrast, and no other sibling reads quality policies.
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?
Usage is implied through the verb 'Read' and the scope 'for the study', but there is no explicit guidance such as 'use when you need to view policies; use create_quality_policy to create them.' The description does not mention alternatives or exclusions, so the agent must infer when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.