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Create Simulation

create_simulation

Predict community reactions by simulating AI agents. Describe scenarios or upload documents to generate calibrated prediction reports.

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?

Describes async behavior (returns immediately with simulation_id), polling mechanism with 50s blocking, and extraction of named entities. Annotations (readOnlyHint=false, openWorldHint=true) are consistent and description adds valuable context beyond them.

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?

Concise with front-loaded purpose, important warnings, and follow-up instructions. No unnecessary sentences; each part adds value.

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

Completeness5/5

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

Covers all key aspects: what the tool does, prerequisites (enrich prompt, upload document), async behavior, status polling, and report availability. No gaps given complexity and schema coverage.

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 coverage is 100%, but description enriches parameters: explains enrichment for prompt, the purpose of document_id, and defaults for platform. Adds clarity beyond schema fields.

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' with specific components (graph build, persona generation, simulation, report). It distinguishes from sibling tools like cancel_simulation, simulation_status, and upload_document.

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?

Provides explicit instructions: enrich the prompt before calling, call upload_document first if user provides a document, and use simulation_status to poll for completion. Clearly states when to use this tool vs. others.

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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