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by getsimba-ai
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Python
Hybrid

List Quality Policies

list_quality_policies
Read-onlyIdempotent

Retrieve quality policies for a study, including full specifications, rule hashes, retirement status, and usage counts, to compare rules and identify the correct policy_id.

Instructions

Read immutable quality policies for the study. Each row carries the full specification plus identity: content_hash (the stored row, including name and rationale), rules_hash (the rules alone: checks sorted by metric plus the protocol, so two policies with the same rules_hash apply the same rules whatever they are called), is_newest, derived_from, created_at, retired_at, usage counts (runs_launched, evaluations, resolutions, champion_acceptances) and checks_summary / protocol_summary. Newest is information, not a recommendation: choose policy_id explicitly. Retired policies stay listed for history but are refused for new launches, assessments and pair reviews. Deleting a policy is possible only for a policy nothing references and only from the signed-in project owner UI, never through this API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
cursorNo
study_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.6.1
    • addedInput schema / properties / cursor
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Cursor"
+}
    • addedInput schema / properties / limit
      Added value: +{
+  "anyOf": [
+    {
+      "type": "integer"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Limit"
+}
  2. Addedv0.5.0

TDQS

A4.1/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already mark the tool as read-only and non-destructive, but the description adds substantial behavioral context: it explains the immutability, the inclusion of retired policies and their refusal status, the meaning of content_hash versus rules_hash, and that deletion is impossible via the API. This goes well beyond the annotation hints and gives the agent a precise model of the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded: the first sentence states the core purpose, and the subsequent sentences explain row semantics and behavioral caveats. Every sentence adds meaningful information, though the level of detail around content_hash and rules_hash is arguably more than needed for a list endpoint. It remains well-organized and scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists, the description need not enumerate return fields; instead it adds semantic context that the schema cannot convey—what the hashes mean, how to treat newest, and the retired-policy behavior. It does not cover pagination ordering or iteration, but these are likely inferable from the cursor/limit parameters. Overall it is sufficiently complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden for parameter explanation. It never mentions limit or cursor, and only indirectly identifies study_id as the study being listed. The pagination parameters are left entirely to inference, and the description adds no format, constraints, or interaction details beyond the raw schema.

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 opens with 'Read immutable quality policies for the study', a specific verb-resource pair that clearly indicates a list operation. It further differentiates from sibling tools like get_quality_policy and diff_quality_policies by describing the full row contents and the fact that it lists all policies, including retired ones. The scope is unambiguous.

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 gives clear context about how to interpret the list: 'Newest is information, not a recommendation' and notes that retired policies are refused for new launches, assessments, and pair reviews. However, it does not explicitly state when to prefer this tool over get_quality_policy or diff_quality_policies, leaving some inference to the agent. The deletion note clarifies a limitation but not alternative tool selection.

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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