A gateway server that bridges Docker and other systems with Model Context Protocol, enabling LLM interactions with containerized environments
Scoring was not performed
Tool name 'find-tools' is a noun phrase, not a verb_noun action. Should follow verb-first naming like 'recommend_tools' or 'search_available_tools'. LLMs infer intent from verbs, noun-starting names reduce clarity.
No output schema documented. LLMs cannot plan downstream actions without knowing what fields to expect. The tool description does not state what structure is returned (array of tool objects? IDs? Full definitions?).
Input parameter 'prompt' lacks format constraints or guidance. It accepts arbitrary free-form text with no enum, length limits, or examples of good prompts. This invites hallucinated or ambiguous requests.
Description does not explain WHEN to call this tool. Does the agent call it once at startup? On every request? When the user asks for tool suggestions? Agents cannot determine selection criteria without this context.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 51 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 25 | - | v1 |
The tool is meta-functional (finding tools) rather than domain-specific (Docker operations). This is atypical for an MCP server and suggests the server may be a gateway/discovery layer rather than a direct capability provider. Unclear if this is the primary role of the server.
Parameter description mentions 'AI will analyze' but does not specify what AI system, whether it uses embeddings, keyword matching, or semantic search. Opacity about internal mechanism reduces trust and makes debugging failures harder.