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databases_create_email_attribute

Add an email attribute to a database collection for storing and validating email addresses, ensuring data integrity through required fields and default values.

Instructions

Create an email attribute.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_idYesDatabase ID.
collection_idYesCollection ID. You can create a new collection using the Database service [server integration](https://appwrite.io/docs/server/databases#databasesCreateCollection).
keyYesAttribute Key.
requiredYesIs attribute required?
defaultNoDefault value for attribute when not provided. Cannot be set when attribute is required.
arrayNoIs attribute an array?
Behavior2/5

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

No annotations are provided, so the description carries full burden. 'Create' implies a write/mutation operation, but the description doesn't disclose any behavioral traits: no mention of permissions required, whether this is idempotent, what happens on conflict (e.g., if attribute key already exists), rate limits, or what the response contains. For a mutation tool with zero annotation coverage, this is a significant gap.

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

Conciseness5/5

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

The description is extremely concise - a single sentence with no wasted words. It's front-loaded with the essential action and resource. While it may be too brief for completeness, it earns full marks for conciseness.

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

Completeness2/5

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

Given this is a mutation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what an email attribute is, when to use it, what permissions are needed, what the response looks like, or how it differs from other attribute types. For a tool with 6 parameters and complex sibling relationships, this minimal description leaves too many gaps.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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

Purpose3/5

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

The description states the action ('Create') and resource ('an email attribute'), which provides basic purpose. However, it doesn't differentiate this from other attribute creation tools (e.g., databases_create_string_attribute, databases_create_boolean_attribute) beyond the 'email' qualifier in the name. The description doesn't explain what an 'email attribute' is or how it differs from other attribute types.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. With multiple sibling tools for creating different attribute types (email, string, boolean, integer, etc.), the description offers no context about when an email attribute is appropriate versus other attribute types. There's no mention of prerequisites or when-not-to-use scenarios.

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

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