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create_decision

Submit a question to a collective of AI agents for multi-perspective voting, producing a consensus decision.

Instructions

Create a new decision for the swarm to evaluate.

Multiple agents can then vote on this decision from different perspectives (financial, legal, technical, market, risk). The hive mind aggregates votes into a collective decision.

Args: question: The decision question (e.g. "Should we launch product X?") context: Background information for the decision decision_id: Custom ID (auto-generated if empty) created_by: Who initiated the decision

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
contextNo
decision_idNo
created_byNo
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that after creation, agents can vote and the hive mind aggregates, giving insight into the tool's lifecycle. However, it does not mention side effects, permissions, or reversibility.

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-structured with a clear purpose statement, a brief process overview, and a formatted Args list. It is slightly verbose in the process paragraph but still concise overall.

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 input parameters and high-level behavior. It lacks output specification (no output schema) and does not mention return values, but given it's a creation tool, the description is reasonably complete for an agent to use.

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

Parameters5/5

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

The Args section adds significant meaning beyond the input schema: it explains that question is the decision prompt, context is background, decision_id can be auto-generated, and created_by identifies the initiator. This fully compensates for the 0% schema description coverage.

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

Purpose5/5

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

The description starts with 'Create a new decision for the swarm to evaluate,' which is a specific verb and resource. It clearly distinguishes this from sibling tools like cast_vote or list_decisions by focusing on creation.

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 context by mentioning subsequent voting and aggregation, but it does not explicitly state when to use this tool versus alternatives, nor does it provide 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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