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The server defines 6 tools for Docker container management with reasonable naming conventions (all start with action verbs: create, execute, save, export, exit). However, quality is significantly degraded by: (1) incomplete input schemas, while parameters are listed, several lack formal type constraints and validation rules; (2) parameter descriptions are generic and do not explain constraints, dependencies, or error conditions; (3) no output schemas are documented, forcing LLMs to infer response structure; (4) error handling is minimal, most tools return free-text error strings with no guidance on recovery; (5) no tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear safety implications (e.g., execute_command_in_container is IRREVERSIBLE, exit_container is DESTRUCTIVE). Tool names are reasonable but descriptions lack guidance on WHEN to use each tool vs. alternatives. Output is free-text rather than structured JSON, reducing composability. The server sits at the threshold between 'poor' (50 - 59) and 'fair' (60 - 69) due to basic structure being present but systematic gaps in completeness.
Create a new container with the specified base image. Args: image: Docker image to use (e.g., python:3.9-slim, ubuntu:latest) Returns: Container ID of the new container
Create a file in the specified container. Args: container_id: ID of the container filename: Name of the file to create content: Content of the file Returns: Status message
Execute a command in the specified container. Args: container_id: ID of the container command: Command to execute Returns: Command output
Stop and remove a running container. Args: container_id: ID of the container to stop and remove force: Force remove the container even if it's running Returns: Status message about container cleanup
Generate a Dockerfile that recreates the current container state. Args: container_id: ID of the container to export Returns: Dockerfile content and instructions
No output schemas documented. All tools return free-text strings rather than structured JSON objects. LLMs cannot reliably parse responses, extract IDs for chaining, or compose tools.
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear safety implications. execute_command_in_container marked IRREVERSIBLE and exit_container marked DESTRUCTIVE in metadata but not annotated in schema. Agents cannot infer retry safety.
Error handling is uniformly weak. All tools return generic error strings ('Error: ...', 'not found') with no recovery guidance. No distinction between retryable (transient failure) vs. user-fixable (invalid input) vs. fatal errors.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 49 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 45 | - | v1 |
Save the current state of a container as a new image. Args: container_id: ID of the container to save name: Name for the saved image (e.g., 'my-python-env:v1') Returns: Instructions for using the saved image
Parameter descriptions lack actionable constraints. E.g., 'image' parameter in create_container_environment has description 'Docker image to use (e.g., python:3.9-slim, ubuntu:latest)' but includes example values, LLMs may reuse literals instead of adapting to user context. No validation rules (image name format, allowed registries, etc.) documented.
No parameter dependency documentation. E.g., if container_id is invalid, subsequent tools fail silently. No guidance on how to discover valid container IDs or how to handle 'Container not found' errors across tools.
No dry-run or confirmation step for destructive operations (exit_container with force=true). Agents cannot preview the effect before committing. Pattern: confirmation-request not implemented.
Missing tool composition guidance. No documentation of which tools should be called in sequence or which tools' outputs chain into subsequent calls. E.g., create_container_environment returns a container ID, but the description does not explicitly state this ID is used by all other tools.
execute_command_in_container returns raw command output as unstructured text. Large outputs can blow context window; no limit is enforced or documented. No pagination/streaming for multi-line responses.