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ai-rete-rag

by zaharajabeen13-create
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Python
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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.4/5.0

    Scored across 8 tools

    Disambiguation4/5

    Each tool has a distinct role: decide runs the engine, list_rules and get_rule_source expose rule views in different formats (readable vs. editable YAML), put_rules and import_policy_rules handle rule authoring vs. drafting, ingest_text and list_documents manage knowledge, and get_usage tracks account limits. There is minor overlap between list_rules and get_rule_source, but their differing output formats make the purpose clear.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: decide, list_rules, ingest_text, list_documents, get_rule_source, put_rules, import_policy_rules, get_usage. Every name clearly indicates the action and the resource, with no mixing of styles or vague verbs.

    Tool Count5/5

    With 8 tools, the set is well-scoped for a rules-and-RAG domain. It covers decision execution, rule inspection/authoring (including import), document management, and account usage without unnecessary bloat or missing critical pieces.

    Completeness4/5

    The surface covers the core lifecycle: rule viewing (list_rules, get_rule_source), rule creation/replacement (put_rules), rule drafting from policy (import_policy_rules), document ingestion/listing, and decision execution (decide). Minor gaps exist—no explicit delete for rules or documents—but put_rules can clear rules by replacing with an empty set, and document deletion is not essential for the primary workflow.

    Maintenance

    ActivitySlowing
    ResponsivenessSlow

    Resources