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create_blackboard

Create a Blackboard asset to give AI a shared memory for Behavior Trees. Define keys for data such as locations, targets, and states.

Instructions

Create a Blackboard asset.

The Blackboard is the AI's shared memory - it stores data that the Behavior Tree reads and writes during execution.

Args: name: Blackboard asset name (e.g., "BB_EnemyAI") keys: List of key dicts: [{"name": "TargetActor", "type": "Object"}, {"name": "PatrolLocation", "type": "Vector"}, {"name": "bIsAlerted", "type": "Boolean"}, {"name": "Health", "type": "Float"}] path: Content browser path

Key types: Object, Actor, Class, Enum, Float, Int, Bool (Boolean), String, Name, Vector, Rotator

KB: see knowledge_base/04_AI_SYSTEMS.md#overview Example: create_blackboard(name="ExampleName")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysNo
nameYes
pathNo/Game/AI

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It states this tool creates an asset, but does not mention whether an existing asset with the same name is overwritten, whether the content path must already exist, if folders are auto-created, permissions needed, or the shape of the result. This is a significant transparency gap for a mutation-style tool.

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

Conciseness4/5

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

The description is well-organized with a front-loaded purpose, a short context paragraph, clearly labeled Args, a key-types list, and an example. It is slightly long but every section adds useful information, and the structure makes it easy to scan.

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

Completeness4/5

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

The description covers the tool's domain, all parameters, valid key types, and a usage example. Since an output schema exists, return values do not need to be explained. The main gap is lack of guidance on interaction with related tools and behavior when assets already exist.

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

Parameters4/5

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

Schema description coverage is 0%, and the description compensates well. It documents all three parameters: name with an example, keys with a concrete dict structure and supported types, and path as the content browser path. This adds substantial meaning beyond the minimal schema.

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 opens with 'Create a Blackboard asset,' a clear verb+resource statement, and goes on to define what a Blackboard is. It does not explicitly distinguish this tool from sibling tools like set_behavior_tree_blackboard or create_behavior_tree, so it is clear but lacks sibling differentiation.

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

Usage Guidelines3/5

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

The description implies usage by explaining that Blackboards are the AI's shared memory used by Behavior Trees, and the example shows a minimal call. However, it does not explicitly state when to use this tool versus alternatives, mention prerequisites, or give when-not-to-use guidance.

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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