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Glama
Acacian

aegis

by Acacian
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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
evaluate_actionA

Evaluate an AI agent action against the current governance policy.

    Returns a decision: auto (allow), approve (needs human review), or block (deny).

    Args:
        action_type: The kind of operation (e.g. "read_file", "send_email", "delete").
        target: The system being acted upon (e.g. "filesystem", "stripe", "database").
        params: Arbitrary parameters for the operation.
        description: Optional human-readable description.
        agent_id: Optional identifier for the agent performing the action.
evaluate_batchA

Evaluate multiple actions at once against the governance policy.

    Each action dict should have: action_type, target,
    and optionally params, description, agent_id.

    Args:
        actions: List of action dicts to evaluate.
get_policyA

Get the current governance policy rules.

Returns all configured rules with their action patterns, targets, risk levels, and approval requirements.

update_policyA

Hot-reload the governance policy from a YAML string.

    The new policy takes effect immediately for all subsequent evaluations.

    Args:
        yaml_content: YAML string containing the policy rules.
check_riskA

Quick risk check for an action type + target combination.

    Returns just the risk level and approval requirement — lighter than evaluate_action.

    Args:
        action_type: The kind of operation.
        target: The system being acted upon.

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

A4.1/5.0

Scored across 5 tools

Disambiguation4/5

Tools have distinct purposes, but 'check_risk' and 'evaluate_action' overlap conceptually as both assess actions, with 'check_risk' being lighter. This could cause confusion, though descriptions help differentiate.

Naming Consistency5/5

All tool names follow a consistent 'verb_noun' pattern in snake_case (e.g., 'check_risk', 'evaluate_action', 'get_policy'), ensuring clarity and predictability.

Tool Count5/5

With 5 tools covering risk checking, single evaluation, batch evaluation, policy retrieval, and policy update, the count is well-scoped for governance policy management.

Completeness5/5

The tool set covers all core operations: quick risk checks, detailed evaluations (individual and batch), and policy management (view and update), with no obvious gaps.

Maintenance

ActivityMaintained
ResponsivenessResponsive

Resources