ai·rete·rag
Server Details
Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.
Glama couldn't complete the latest health check. If this server requires authentication, missing or expired test credentials may be the cause. A test profile lets Glama authenticate for health checks and discover tools; it is separate from your personal connections.
If you are the author, claim ownership, then add or update a test profile under Admin → Test Profile.
- Status
- Unhealthy
- Uptime
- 14.4% over 49 days
- 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
Scored across 8 tools
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.
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.
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.
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.
8 tool updates
- First observed
decide - First observed
get_rule_source - First observed
get_usage - First observed
import_policy_rules - First observed
ingest_text - First observed
list_documents - First observed
list_rules - First observed
put_rules
Related MCP Connectors
Turn a written policy into rules a program can check, and get the same answer every time.
The system of record for AI agent authority: playbooks, routed policy questions, reusable rules.
Deterministic decision layer for autonomous agents: reproducible PROCEED/REVIEW/SKIP verdicts.
AI agent policy decisions: ALLOW, DENY, APPROVE, ESCALATE. 192 jurisdictions, signed evidence.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceDeterministic policy enforcement for AI agent tool calls. It evaluates every tool call against user-defined rules before execution, with no LLM in the authorization path.3MIT
- 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
AlicenseAqualityAmaintenanceEnables clients to run deterministic eligibility and decision evaluations from compiled legislation, policy, contracts, and regulation, inspect schemas and traces, route next questions, and discover rulesets, with optional private-beta authoring tools for rule generation, testing, refining, and publishing.35632 npm1MIT
Glama MCP Gateway
Add one secure layer between your agents and this server.