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Prepare Simulation

mirofish_prepare_simulation

Prepares a simulation by reading graph entities, generating LLM-based agent profiles, and creating simulation config. Returns task ID for async operation.

Instructions

Prepare a simulation environment — reads entities from the graph, generates agent profiles via LLM, and creates simulation config. Async — returns task_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typesNoFilter to specific entity types
simulation_idYesThe simulation ID
force_regenerateNoForce regeneration even if already prepared
use_llm_for_profilesNoUse LLM to generate agent personality profiles
parallel_profile_countNoNumber of profiles to generate in parallel
Behavior3/5

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

Annotations already indicate readOnlyHint=false and openWorldHint=true, so mutation is known. The description adds useful context about async execution, LLM generation, and the creation of a config, which goes beyond annotations. However, it does not explain side effects like what happens if already prepared or the cost/time implications of LLM calls.

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 concise sentence that leads with the main purpose and lists key steps in a readable sequence. No redundant words or filler.

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 complex async tool with no output schema, the description covers the core workflow and return value (task_id). However, it omits how to check progression (e.g., prepare_status), prerequisites like existing entities, and the effect of force_regenerate on an already-prepared environment. These gaps reduce completeness.

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 coverage is 100%, so all five parameters are described in the input schema itself. The description only loosely connects to parameters (e.g., 'LLM' implies use_llm_for_profiles) but adds no new meaning beyond the schema descriptions.

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 states a specific action ('Prepare a simulation environment') and outlines the concrete steps (reads entities, generates profiles via LLM, creates config). This clearly distinguishes it from sibling tools like create_simulation and start_simulation.

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?

No guidance is provided on when to call this tool versus alternatives (e.g., after create_simulation, before start_simulation). There is no mention of prerequisites or exclusions. The async behavior hints at a follow-up via prepare_status but that is not explicitly stated.

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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