Schema | aegis

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

Features and capabilities supported by this server

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
evaluate_action	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_batch	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_policy	Get the current governance policy rules. Returns all configured rules with their action patterns, targets, risk levels, and approval requirements.
update_policy	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_risk	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.`

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

aegis