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populate_star_system_celestial_bodies

Generate planets, moons, and other celestial bodies for a star system by extracting them with an LLM, with optional synchronous waiting for discovery to complete.

Instructions

Discover and generate celestial bodies (planets, moons) for a star system via LLM extraction.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "populate_star_system_celestial_bodies"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesStar System ID
syncNoIf true, wait synchronously for celestial body discovery to complete

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does add one genuinely useful behavioral fact – generation is LLM-based, implying non-deterministic and potentially slow/costly output – but it omits whether bodies are persisted, whether existing bodies are replaced or preserved on re-run, and whether the operation is idempotent. The sync parameter is covered only by the schema, not the description.

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?

A single front-loaded sentence that identifies the action, the resource, the scope, and the mechanism with no filler. The parenthetical scope hint (planets, moons) is compact and informative rather than redundant.

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?

The tool is simple (2 params, fully documented schema, no output schema), so the bar is modest. Still, for a generative mutation an agent would benefit from knowing whether the result is persisted and what it gets back (generated bodies, counts, or a job handle when sync=false); none of that is stated.

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 description coverage is 100%, so the schema already documents both 'id' (Star System ID) and 'sync' (wait synchronously). The description adds only the framing that the id identifies the system whose bodies are populated, and says nothing about the consequence of sync=true vs false beyond what the schema states. Baseline 3 is appropriate.

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?

Names a specific verb pair (discover and generate) and a specific resource (celestial bodies: planets, moons) scoped to a star system, plus a distinctive mechanism ('via LLM extraction'). It does not, however, distinguish itself from close siblings such as populate_celestial_bodies_bulk or create_celestial_body, so an agent cannot tell from the description alone why it should pick this one.

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?

Usage is only implied: the singular 'a star system' plus a required id suggests one-system-at-a-time use, as opposed to the bulk population sibling. No explicit when-to-use or when-not-to-use guidance and no alternative tools are named, leaving the agent to infer the single-vs-bulk distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.