ai-rete-rag
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- FlicenseAqualityDmaintenanceSemantic search over project-specific decision rules using Chroma, enabling LLMs to retrieve relevant context.1-
- AlicenseNot gradedqualityBmaintenanceEnables users to run deterministic rule checks against their own metrics or HTTP/CSV sources and pull back a short decision brief: only the items that crossed a threshold, each with the number that tripped it and suggested next steps. Users can also record accept/reject/edit feedback so subsequent briefs reflect their own playbook, all without API keys or a live LLM.MIT

aethis-mcpofficial
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TDQS
Scored across 9 tools
Most tools are clearly distinct across the decision lifecycle: inspecting parameters, listing rules, fetching editable rule YAML, deciding, ingesting documents, drafting rules, saving rules, and checking usage. However, get_parameters, list_rules, and get_rule_source all expose rule/domain metadata and can overlap when an agent is trying to learn which facts or rules matter before calling decide.
All tool names use consistent snake_case and are predominantly imperative verb_noun forms such as get_parameters, list_rules, put_rules, ingest_text, and import_policy_rules. The single verb decide is the one deviation, but it is clear and consistent with the server's action-oriented naming style.
Nine tools is well scoped for a rule-based RAG decision server, covering discovery, authoring, ingestion, decision-making, and account usage without feeling bloated. Each tool appears to support a real part of the workflow.
The core lifecycle is covered: inspect parameters and rules, author/import/save rules, ingest and list documents, make decisions, and check usage. Minor gaps exist around domain/document management, such as no explicit list_domains or document deletion/read-content tool, though list_rules across all domains may partially mitigate this.