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Aeronique

llm-redteam-mcp

by Aeronique
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Python
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    Deploy Server
    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 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: listing models, listing probes, running batch probes, running single prompts, and exporting reports. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (export_, list_, list_, run_, run_) using snake_case, making the naming predictable and easy to understand.

    Tool Count5/5

    With 5 tools covering model listing, probe listing, batch and single execution, and report export, the tool set is well-scoped for an LLM red-teaming server. No unnecessary tools or obvious omissions.

    Completeness4/5

    The core workflow of listing resources, running probes, and exporting results is covered. A minor gap is the lack of a tool to view report contents without writing to disk, but the export tool returns file paths that an agent can use with other tools to read files.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources