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Glama
Aeronique

llm-redteam-mcp

by Aeronique
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
LLM_REDTEAM_PROBE_DIRNoProbe library directory../probes
LLM_REDTEAM_OLLAMA_URLNoOllama base URL.http://localhost:11434
LLM_REDTEAM_REPORTS_DIRNoWhere export_report writes../reports

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
list_modelsA

List the LLM models installed in the local Ollama instance.

Returns a mapping with a ``models`` list (name, size, family, parameters),
or an ``error`` string if Ollama cannot be reached.
list_probesA

List every probe category and the probes it contains.

Returns a mapping of category name to its description and probe metadata
(id, description, whether it installs a system prompt, and its fail
markers), or an ``error`` string if the probe library is invalid.
run_probeA

Run every probe in category against model and score the results.

Args:
    model: Name of an installed Ollama model (see ``list_models``).
    category: A probe category name (see ``list_probes``).

Returns:
    A report dict (model, scope, summary, per-probe results), or an
    ``error`` string if the category is unknown or the library is invalid.
run_singleA

Run one ad-hoc prompt against model and score the response.

No fail markers are defined for an ad-hoc prompt, so the verdict will be
``pass`` only if the model clearly refuses, and ``needs_review`` otherwise.

Args:
    model: Name of an installed Ollama model (see ``list_models``).
    prompt: The single prompt to send.

Returns:
    A report dict with one result, or an ``error`` string on failure.
export_reportA

Write the most recent run to timestamped JSON and Markdown in ./reports.

Returns:
    A mapping with the written ``json`` and ``markdown`` file paths, or an
    ``error`` string if no run has been performed yet this session.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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