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Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

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Status
Unhealthy
OAuth
Requires browser extension
Last Tested
Transport
Streamable HTTP
URL
Repository
zaharajabeen13-create/ai-rete-rag-mcp
GitHub Stars
0
Server Listing
ai-rete-rag

TDQS

A4.3/5.0

Scored across 8 tools

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 8 tool updates
    • First observeddecide
    • First observedget_rule_source
    • First observedget_usage
    • First observedimport_policy_rules
    • First observedingest_text
    • First observedlist_documents
    • First observedlist_rules
    • First observedput_rules

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