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ai·rete·rag

by com.ai-rete-rag

List rules

list_rules
Read-only

List the decision rules for one domain (or all domains).

Returns each rule's conditions — either a flat AND list (field / operator / value) or a when condition tree (nested all/any/not) — plus its verdict, salience, and any asserted facts (action.assert, the facts a rule produces for other rules to consume). edges lists the derived rule→rule dependencies: src asserts a fact type that dst's conditions test (forward chaining). Each rule may also carry citation — the policy sentence it encodes — which is what lets a decision be traced back to the source clause. Also includes overlap warnings. Use this to learn which fact fields a domain expects before calling decide.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnlyHint annotation: it explains the return structure (flat AND list or when tree), verdict, salience, asserted facts, rule→rule dependencies, citations, and overlap warnings. This gives the agent a realistic expectation of what the tool returns and its analytical value.

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 front-loaded with the main purpose and then provides dense, relevant detail in a structured way. Every clause adds information about returned data or usage context. Length is justified by the complexity of the output.

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?

Given the output schema exists and the tool has one optional parameter, the description is complete enough. It covers purpose, parameter meaning, return-value highlights, and the intended usage before decide. No critical gaps are apparent.

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?

With zero schema description coverage, the description compensates by clarifying the only parameter: 'one domain (or all domains)' maps to the optional domain field and its null default. It also ties domain to the decide workflow. This is sufficient for a single free-form string parameter, though it does not enumerate possible domain values.

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 a specific verb and resource: 'List the decision rules for one domain (or all domains).' It clearly identifies the scope and differentiates from sibling tools like put_rules (write/update) and decide (rule execution).

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 explicitly states when to use this tool: 'Use this to learn which fact fields a domain expects before calling decide.' This gives a clear usage context and points to a related sibling. It does not explicitly mention when not to use alternatives, but the primary workflow guidance is strong.

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

A4.3/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: decide executes decisions, rule management tools (get_rule_source, list_rules, put_rules) each address different aspects of rule viewing/editing, import_policy_rules handles policy-to-rule drafting, and document tools handle knowledge base content. No two tools are easily confused.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern, e.g., list_rules, get_usage, put_rules, import_policy_rules. The single-verb 'decide' fits naturally as the core action. There is no mixing of styles or unpredictable naming.

Tool Count5/5

With 8 tools, the server is well-scoped for its purpose of rule-based decisioning with RAG support. Each tool represents a distinct capability in the workflow—policy ingestion, rule authoring, knowledge base management, decision execution, and usage monitoring—without excess.

Completeness4/5

The tool set covers the core lifecycle: ingest policy text, import draft rules, publish rules, list rules/source, and execute decisions. Minor gaps exist such as no explicit delete for documents or rules (though put_rules with empty YAML can effectively clear rules), and domain listing is indirect via list_rules. These are workable omissions.

Resources