Skip to main content
Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
AI_GUARDIAN_AIOPS_HOMENoOverride the home directory for audit.db and usage.db
AI_GUARDIAN_AUDIT_RATIONALENoRationale for high-risk operations
AI_GUARDIAN_AUDIT_APPROVED_BYNoName of the approver for high-risk operations
AI_GUARDIAN_AIOPS_MASTER_PASSWORDNoOnly if a target has a stored token

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

[READ] Installed models, each annotated with the allow/deny policy verdict.

Shadow (unsanctioned) models show allowed: false.

Args: target: Ollama target name from config; omit for the default (local).

running_modelsA

[READ] Currently loaded models: VRAM footprint + residency expiry.

Args: target: Ollama target name from config; omit for the default.

model_detailsA

[READ] License / parameters / capabilities for one model.

Args: model: Model name (e.g. "llama3.2:3b"). target: Ollama target name from config; omit for the default.

server_statusB

[READ] Ollama reachability + version.

Args: target: Ollama target name from config; omit for the default.

vram_usageA

[READ] Total VRAM used by loaded models; flag over-budget.

Args: budget_bytes: Optional VRAM budget; models over it are flagged. target: Ollama target name from config; omit for the default.

pull_modelA

[WRITE][risk=medium] Pull a model — refused if it violates the deny/allow policy.

Args: model: Model name to pull. target: Ollama target name from config; omit for the default.

remove_modelA

[WRITE][risk=high] Delete a local model. Destructive — pass dry_run=True to preview.

Captures the model's manifest so the harness records an undo (re-pull).

An undo is recorded only when the allow/deny policy would permit re-pulling the model. For a denied one the result says reversible=false and explains why, rather than recording a re-pull that undo_apply is bound to refuse. The dry-run preview carries the same verdict, so the caller learns it before the deletion rather than after.

Args: model: Model name to delete. dry_run: If True, return what WOULD be deleted without deleting. target: Ollama target name from config; omit for the default.

unload_modelA

[WRITE][risk=medium] Evict a model from VRAM (keep_alive:0).

Args: model: Model name to unload. target: Ollama target name from config; omit for the default.

scan_promptA

[READ] Pure content scan of a text for secrets / PII / code / jailbreak.

No Ollama call — a deterministic scan returning findings + a weighted risk band. Use it to pre-check anything before sending it to a local model.

Args: text: The text to scan.

usage_eventsA

[READ] Query the observed-usage log (route-through prompts + their findings).

Args: model: Filter by model name. risk_level: Filter by risk band (none/low/medium/high/critical). allowed: True = only allowed calls; False = only blocked. since: ISO start timestamp. limit: Max rows.

Returns {"events": [...], "count": N, "returned": N, "limit": L, "truncated": bool}. When truncated is true more prompts were observed than were returned — raise the limit or narrow the filters. Never conclude "no risky prompts were observed" from a truncated result.

anomaly_reportB

[READ] Rollup: shadow models, digest drift, high-risk prompts, blocked count.

Args: target: Ollama target name from config; omit for the default.

guarded_generateA

[WRITE][risk=medium] Scan + policy-gate a prompt, record it, then run if allowed.

The route-through guard: scans the prompt (secrets/PII/code/jailbreak), checks the model against policy, blocks if the risk band >= block_threshold or the model is disallowed, records the interaction to the usage log, and only calls Ollama if allowed. The raw prompt is never stored.

Args: model: Model to run. prompt: The prompt text (scanned before any model call). agent / user: Actor attribution recorded in the usage log. block_threshold: Block when risk band >= this (none/low/medium/high/critical). target: Ollama target name from config; omit for the default.

observe_chatA

[WRITE][risk=medium] Scan + policy-gate a chat exchange, record it, then run if allowed.

Args: model: Model to run. messages: OpenAI-style [{"role","content"}] messages (contents are scanned). agent / user: Actor attribution recorded in the usage log. block_threshold: Block when risk band >= this. target: Ollama target name from config; omit for the default.

policy_viewA

[READ] The current model allow/deny policy + provenance digest pins.

model_provenanceA

[READ] Compare each installed model's digest against its pin; flag drift.

Args: target: Ollama target name from config; omit for the default.

set_model_allowlistA

[WRITE][risk=medium] Replace the model allowlist (glob patterns; empty = allow-all).

Args: models: Shell-glob patterns of permitted model names (e.g. ["llama3.", "qwen"]).

set_model_denylistA

[WRITE][risk=medium] Replace the model denylist (deny patterns always win).

Args: models: Shell-glob patterns of forbidden model names.

pin_model_digestA

[WRITE][risk=medium] Pin a model's expected provenance digest (drift detection).

Args: model: Model name. digest: The expected digest to pin (from list_models / model_details).

proxy_guidanceA

[READ] How to run the transparent capture proxy, and what it does NOT guarantee.

WRITES NOTHING and starts no listener — it composes the ai-guardian proxy serve command plus the client-side change, and returns the caveat that matters: the proxy is a CHOKEPOINT, not an enforcement boundary. Any client that can still reach the runtime's real port bypasses it entirely, and this tool cannot detect that. Captured traffic is a sample, not the population, until the runtime is unreachable except through the proxy.

Requests are scanned before forwarding; responses stream through uninspected, because buffering them would turn every streaming client into a blocking one. A governed request whose body cannot be parsed is refused, not forwarded.

Args: listen_host: Address the proxy would bind. listen_port: Port the proxy would bind (default 11435). target: Runtime target from config; omit for the default.

undo_listA

[READ] List recorded, not-yet-applied undo tokens (most recent first).

Each entry names the original tool, the inverse tool that undo_apply would run, and a human note. Use the undoId with undo_apply.

Returns {"undos": [...], "returned": N, "limit": L, "truncated": bool}. truncated is measured (one extra row is fetched), not guessed from a length coincidence: when it is true there are MORE tokens than shown, so re-run with a higher limit rather than reporting the list as complete.

Each entry carries effectVerified. False means the original write lost its response, so the change it reverses is PROBABLE, not confirmed — check the live state before applying, and do not report the result as a restore of a state that may never have been reached.

Args: limit: Max rows to return (default 50, capped at 500). target: Unused (undo state is host-local); accepted for CLI uniformity.

undo_applyA

[WRITE][risk=medium] Apply a recorded undo by dispatching its inverse tool.

The inverse runs through its own governed tool, so its real risk tier is recorded there. Pass dry_run=True to preview the inverse call without executing it. A token can only be applied once.

Args: undo_id: The undoId from undo_list (or an _undo_id in a write result). dry_run: If True, preview the inverse tool + params without running it. target: Passed through to the inverse tool when it accepts a target.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/AIops-tools/AI-Guardian'

If you have feedback or need assistance with the MCP directory API, please join our Discord server