Skip to main content
Glama

container_wait

Read-only

Block execution until a Docker container reaches a specified state: stopped, healthy, or matching a log pattern. Returns status without raising on timeout.

Instructions

Block until a container reaches a condition: stopped, "healthy", or its logs contain a pattern.

One contract for every mode: never raises on timeout — the result always carries met (condition reached) and timed_out. The stop conditions ("not-running"/"next-exit"/"removed") use the daemon's blocking wait and fill status_code/error (the container's exit info); "healthy" polls the container's HEALTHCHECK every poll_intervals and fills health/status; "log-match" polls recent logs every poll_intervals for pattern and fills matched_line. For a compose project use compose_wait; for swarm services use service_wait.

Health semantics: with no HEALTHCHECK defined, once the container is running the tool returns promptly with health: null and met: false (false = "not confirmed healthy", not "unhealthy" — check health to tell them apart). A container that exits before becoming healthy returns its terminal status and met: false.

Log-match semantics: pattern is matched as a plain substring by default — safe against any input, including adversarial ones. Pass regex=True to match pattern as a regular expression (via re.search) instead; only do this with patterns you trust, since a regex with catastrophic backtracking run against attacker-influenced log content can exhaust CPU (ReDoS). Checks stdout and stderr, most recent lines first within each poll. If the container exits/dies before the pattern ever appears, returns promptly with met=false (not timed_out) — no further logs can arrive, so there's nothing to keep polling for.

args: id_or_name - The container id or name until - Condition to wait for: "not-running" (default), "next-exit", "removed", "healthy", or "log-match" (requires pattern) timeout_seconds - Max seconds to wait before returning with timed_out=true (default 600) poll_interval - "healthy"/"log-match" only: seconds between re-checks (default 2, > 0); capped by the time left so a large value can't push the total wait past the timeout pattern - "log-match" only: substring (or, with regex=True, a regular expression) to look for in the container's logs regex - "log-match" only: treat pattern as a regular expression instead of a plain substring returns: dict - {"container", "until", "met", "timed_out", "status_code", "error", "health", "status", "matched_line", "waited_seconds"}; stop modes fill status_code/error, "healthy" fills health ("starting"/"healthy"/"unhealthy", or null with no healthcheck) and status, "log-match" fills matched_line when met and status if the container exited without matching.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
regexNo
untilNonot-running
patternNo
id_or_nameYes
poll_intervalNo
timeout_secondsNo
Behavior5/5

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

The description goes far beyond the annotations (readOnlyHint, destructiveHint) by detailing behaviors: never raises on timeout, result always has 'met' and 'timed_out', specific behaviors for each condition (e.g., health semantics, log-match polling), and even a security warning about ReDoS for regex patterns. This is exceptionally transparent.

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

Conciseness4/5

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

The description is well-structured with a one-line summary, contract, condition details, security note, and parameter listing. It is thorough but could be slightly more concise (e.g., some redundancy in return value description). However, the length is justified by the tool's complexity.

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?

Despite no output schema and basic annotations, the description covers all aspects: conditions, timeout behavior, polling details, edge cases (no healthcheck, container exit before pattern), security implications, and return values. It effectively acts as full documentation for the tool.

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?

With schema description coverage at 0%, the description fully compensates by explaining each parameter in an 'args' section: id_or_name, until, timeout_seconds, poll_interval, pattern, regex. It provides defaults, constraints (e.g., poll_interval > 0, capped by timeout), and behavior (e.g., pattern treated as substring, regex flag toggles re.search). This adds significant meaning beyond the raw schema.

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 clearly states the tool waits for a container condition, listing exact conditions ('stopped, healthy, log-match'), which specifies the verb and resource. It explicitly distinguishes from siblings by mentioning 'compose_wait' for compose projects and 'service_wait' for swarm services, differentiating itself clearly.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool (to wait for container conditions) and explicitly mentions alternatives ('compose_wait' and 'service_wait') for other scenarios. However, it does not explicitly state when not to use the tool, which is implied but not directly stated.

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

Install Server

Other Tools

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/GavinLucas/docker-mcp'

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