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system_events

Read-only

Monitor Docker activity by streaming real-time events with filters, limits, and timeout bounds. Use it to react to container or service changes without polling snapshots.

Instructions

Stream real-time events from the Docker server, bounded by limit events or timeout_seconds.

Returns when limit events are collected or timeout_seconds elapses, whichever comes first (limit caps memory; timeout_seconds caps how long the call blocks — without it a quiet daemon would block indefinitely, since the stream only yields on an actual event).

Caveat for ssh:// daemons: docker-py can't cancel an SSH stream, so the timeout_seconds watchdog can't interrupt a fully idle stream — bound with until/limit (or a non-SSH endpoint).

"Wait for the next matching event" idiom: pass limit=1 with filters narrowed to what you care about (e.g. {"type": "container", "event": "health_status"}) and a generous timeout_seconds. This blocks until that one event arrives (or the timeout elapses, returning an empty list) instead of re-polling a snapshot on a timer — there's no separate wait tool for this since the filtering this call already does covers it.

args: since - Show events created since this timestamp until - Show events created until this timestamp filters - Filters to apply to the event stream limit - Max events to return (default 100) timeout_seconds - Max wall-clock seconds before returning what was collected (default 30) returns: list - A list of decoded event dicts (length <= limit)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
sinceNo
untilNo
filtersNo
timeout_secondsNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Even with annotations declaring readOnlyHint=true and destructiveHint=false, the description adds significant non-obvious behavioral details: the stream blocks indefinitely without a timeout, the timeout returns an empty list, and the SSH caveat about docker-py's inability to cancel streams. This rich context is invaluable for the agent and does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is slightly long but every sentence earns its place: it covers the core behavior, the SSH caveat, a practical idiom, and parameter explanations. The structure with clear paragraphs and a labeled args/returns block makes the content easy to parse without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (streaming, timeout boundaries, potential indefinite blocking, SSH limitations, and a waiting idiom), the description covers all essential aspects. It even mentions the return type (list of decoded event dicts) and the length bound, which is sufficient, especially since there is no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero property descriptions (0% coverage), so the description carries full responsibility. It provides a clear args list explaining each parameter's purpose, plus defaults and behavioral relationships (e.g., 'limit caps memory', 'timeout_seconds caps how long the call blocks'). It even includes an example filter structure, fully compensating for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Stream real-time events from the Docker server', which clearly states the verb (stream), resource (real-time events from the Docker server), and is distinct from sibling tools like system_ping or system_info that provide snapshots rather than streams. The `limit`/`timeout_seconds` bounding adds immediate scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it explains the 'Wait for the next matching event' idiom with a concrete example, states that there is no separate wait tool, and includes a specific caveat for SSH daemons with a workaround. This goes beyond generic context and directly tells the agent when and how to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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