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Create Simulation

create_simulation

Run a multi-agent simulation to predict community reactions to events or policies. Generates personas, simulates debates, and delivers a calibrated prediction report.

Instructions

Run a swarm prediction — graph build, persona generation, multi-agent simulation, report.

IMPORTANT: Enrich the prompt before calling. The engine extracts named entities to create personas. Add specific people, companies, organizations, and opposing viewpoints. Show the enriched prompt to the user for confirmation first.

If the user provides a document (PDF, MD, TXT), call upload_document first and pass the returned document_id.

Returns immediately with simulation_id. Call simulation_status to wait for completion — each call blocks up to 50s for the next state change, so you only need a few. When status returns state=COMPLETED, the full report is included inline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesScenario description. E.g. 'How will crypto twitter react to a new ETH ETF rejection?'
presetNoSimulation preset: quick (10 agents, 20 rounds), standard (20/40), deep (50/72)
agent_countNoOverride agent count
roundsNoOverride simulation rounds
platformNoTarget platform(s). Default: both
document_idNoID of a pre-uploaded document (from upload_document tool). Skips file upload and uses server-side sanitized text.
Behavior5/5

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

The description adds behavioral context beyond annotations: it mentions returning immediately with simulation_id and the need to poll simulation_status (blocking up to 50s). It also explains the enrichment and document workflow, which is not evident from annotations.

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 a single paragraph that front-loads the main purpose and then provides important usage instructions. It is mostly concise, though the 'IMPORTANT' section is lengthy but justified by the critical enrichment steps.

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?

Given the complexity (6 parameters, no output schema), the description covers the main workflow: enrichment, document handling, immediate return, and polling. It lacks error handling details but is sufficient for correct invocation.

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?

With 100% schema coverage, the description adds extra meaning: it reinforces prompt enrichment, explains document_id as output of upload_document, and clarifies the simulation flow. This significantly enhances parameter understanding.

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 clearly states 'Run a swarm prediction — graph build, persona generation, multi-agent simulation, report.' This provides a specific verb and resource, distinguishing it from sibling tools like simulation_status or cancel_simulation.

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

Usage Guidelines5/5

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

The description explicitly instructs to enrich the prompt, show it to the user for confirmation, and call upload_document first if documents are provided. It also explains to call simulation_status for completion, giving clear guidance on when and how to use the tool and alternatives.

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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