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adbutler

AdButler MCP Server

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by adbutler

create_data_key

Create a data key for custom targeting in AdButler by specifying a unique name and value type, enabling precise ad delivery.

Instructions

Create a new data key for custom targeting

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesData key name (max 32 chars, must be unique)
typeYesData key value type
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'create' with no mention of auth requirements, idempotency, error behavior (e.g., failure if name already exists), or what happens to existing data keys. The uniqueness constraint appears only in the schema, not in the description, leaving the agent without important behavior details.

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 a single, front-loaded sentence that directly states the action and resource. Every word earns its place; there is no redundancy or filler. This is ideal conciseness for a simple tool.

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

Completeness3/5

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

For a simple 2-parameter create operation with full schema coverage and no output schema, the description is minimally sufficient. However, it omits context about uniqueness enforcement, return value, or typical usage (e.g., creating targets afterwards). Given the simplicity, it's adequate but with clear gaps that prevent a higher score.

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 both parameters (name and type) are fully documented in the schema. The description itself adds no parameter-specific information beyond what the schema already provides. Baseline 3 is appropriate since the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the verb 'Create' and the resource 'data key', with the purpose 'for custom targeting'. This distinguishes it from sibling tools like create_data_key_target, which creates a target rather than a key. However, it lacks additional context about the key's role or scope, so it's not a 5.

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?

The description provides no guidance on when to use this tool versus alternatives, such as update_data_key or create_data_key_target. There are no exclusions, prerequisites, or context about the typical workflow (e.g., create key before creating targets). This is a clear gap.

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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