Dokploy MCP Server

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

Annotations indicate this is a read-only, non-destructive, idempotent, and open-world operation, which the description does not contradict. However, the description adds minimal behavioral context beyond these annotations—it only specifies a GET method. It lacks details on error handling, rate limits, authentication needs, or what specific project data is returned, leaving gaps in understanding the tool's behavior despite the annotations covering basic safety.

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

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

The description is concise and front-loaded, stating the operation and parameters in a clear, two-line format without unnecessary words. However, its brevity borders on under-specification, as it omits critical details like the tool's purpose and usage context, which reduces its overall effectiveness despite the efficient structure.

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

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

Given the tool's simplicity (one parameter) and rich annotations, the description is incomplete. It lacks output details (no output schema provided), fails to explain the return value or potential errors, and does not clarify the tool's role within the broader context of project management tools. This leaves significant gaps for an AI agent to understand how to use the tool effectively.

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

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

The description lists 'projectId' as a required string parameter, but with 0% schema description coverage, it does not add meaningful semantics beyond what the input schema already specifies (type, minLength). It fails to explain what a 'projectId' is (e.g., format, source, or examples), how to obtain it, or its role in the operation, leaving the parameter poorly understood despite the schema's structural details.

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

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

The description '[project] project.one (GET)' is tautological, essentially restating the tool name 'dokploy_project_one' with minimal additional context. It indicates a GET operation on a project resource but lacks specificity about what 'project.one' entails (e.g., retrieving details, status, or metadata). It does not distinguish this tool from sibling tools like 'dokploy_project_all' or 'dokploy_project_search', leaving the purpose vague.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context (e.g., after creating a project), or comparisons with sibling tools such as 'dokploy_project_all' for listing projects or 'dokploy_project_search' for filtered searches. This absence of usage instructions could lead to incorrect tool selection by an AI agent.

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