Get a schema's current fields using its ID (step 1 of schema field management).
Args: schema_id: The ID of the schema to retrieve project_key: Project key (tenant ID). Uses global tenant_id if not provided
Returns: JSON string with schema fields and metadata
| Name | Required | Description | Default |
|---|---|---|---|
| schema_id | Yes | ||
| project_key | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of disclosing behavioral traits. It clearly indicates the return type ('JSON string with schema fields and metadata') and acknowledges using 'global tenant_id if not provided', which is useful. However, it does not mention error behavior (e.g., if schema_id is not found), authentication requirements, or side effects. For a simple read-only getter, this is adequate but not rich in behavioral context.
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 efficiently structured and front-loaded. The first sentence immediately states the purpose, followed by a concise Args section and a Returns line. No word is wasted; every sentence contributes to understanding the tool. The structure follows a conventional docstring format that is easy for an agent to parse.
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 (2 parameters, no nested objects, output schema present), the description covers the essentials: purpose, parameters, and return format. It also contextualizes the tool within a workflow ('step 1 of schema field management'). It does not elaborate on error scenarios or prerequisites, but for a read-only getter with an output schema, the description is sufficiently complete.
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 input schema has no parameter descriptions (0% schema_description_coverage), so the description's Args section provides essential meaning. It explains that schema_id is 'the ID of the schema to retrieve' and clarifies that project_key is the 'Project key (tenant ID)' with a fallback to global tenant_id. This adds value beyond the schema, which only lists types and titles. It doesn't provide exhaustive formatting details, but effectively covers the semantics.
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 tool's primary function: 'Get a schema's current fields using its ID'. It uses a specific verb ('get'), identifies the resource ('schema's current fields'), and specifies the identifier method ('using its ID'). It also distinguishes itself from sibling tools like 'list_schemas' (which lists all schemas) and 'update_schema_fields' (which modifies fields) by positioning itself as 'step 1 of schema field management'.
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 contextual usage guidance by labeling this as 'step 1 of schema field management', implying it should be used before editing fields. It also mentions the optional project_key fallback to global tenant_id, which helps in mixed-tenant scenarios. However, it does not explicitly exclude alternatives or mention when NOT to use this tool, such as when retrieving all schemas (use list_schemas) or when modifying fields (use update_schema_fields).
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.
