Create a new module for translation in a project.
Args: module_name: Name of the module to create project_key: Project key (tenant ID). Uses global tenant_id if not provided
Returns: JSON string with module creation result including module ID and name
| Name | Required | Description | Default |
|---|---|---|---|
| module_name | Yes | ||
| project_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. It does mention a key behavior (project_key uses global tenant_id if not provided) and the return format, but it does not cover potential errors, idempotency, or permissions. This is adequate but not rich.
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 with the action, followed by a clean Args/Returns structure. Every sentence adds value, and there is no redundant information.
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?
For a 2-parameter tool with an output schema, the description covers purpose, parameters, and return value. It does not explain prerequisites or edge cases, which might be relevant for a creation tool, but the overall context is sufficiently complete for an AI agent to use it correctly.
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?
Schema description coverage is 0%, so the description must compensate. It defines both parameters: module_name as the name to create, and project_key as the tenant ID with its defaulting behavior. This adds meaning beyond the bare schema property names, though the default behavior could be more detailed.
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 clearly states the action ('Create') and the specific resource ('a new module for translation in a project'). This distinguishes it from sibling tools like get_translation_modules (which retrieves modules) and save_module_keys_with_translations (which saves keys).
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 context of use is clear: it's for creating a translation module within a project. However, it does not explicitly name alternative tools or state when not to use this tool, so it misses the exclusionary guidance of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools are organized into clear domains (auth, roles, schemas, translations, projects), and within each domain, tools have distinct purposes (e.g., get_schema vs list_schemas, save_captcha_config vs update_captcha_status). A few pairs like get_auth_status and get_authentication_config could cause minor confusion, but their descriptions clarify the difference.
All tool names follow a consistent verb_noun pattern in snake_case, using predictable verbs like get, list, create, update, set, enable, and configure. Even longer names like save_module_keys_with_translations adhere to the same convention, with no mixed casing or arbitrary styles.
With 36 tools, the server exceeds the typical well-scoped range for MCP servers. While the broad platform domain justifies some breadth, the sheer volume can overwhelm agents and suggests the toolset could be consolidated or split into smaller, focused servers.
The toolset provides decent coverage for creation, listing, and some updates across entities like permissions, roles, schemas, and translations. However, there are notable gaps: no delete operations for most resources, no role update, and no project update/delete, which leaves lifecycle management incomplete.
