Skip to main content
Glama
Sign In
sts1

sts1

by KeiNodens
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_decision_pack

Retrieve a compact, structured game-state feed for Slay the Spire AI turns, including card effects, incoming and max damage, lethal targets, and suggested actions.

Instructions

Get a compact decision pack — the primary LLM feed.

Contains NO free-text fields: cards are identified by id and expressed as structured effects (damage/block/draw/apply_power ops from static card analysis + curation rules), plus advisor output: incoming damage, max damage, lethal targets, and a suggested action sequence. Custom mod mechanics appear as separate resources (e.g. hsr toughness is mod_damage pool=toughness, never mixed into HP lethal math).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are present, so the description carries the full burden. It does disclose meaningful traits — no free-text fields, cards keyed by id, advisor output shape, and the separation of mod resources (toughness never mixed into HP lethal math) — but much of this restates what the output schema covers, and it says nothing about side effects, safety, or cost of calling.

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?

Front-loaded: the core statement comes first, then structured detail. It is reasonably tight, though the closing mod-mechanics example is quite domain-specific and borders on detail that could live elsewhere.

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?

For a zero-parameter tool with a full output schema, the definition gives ample orientation about what the pack contains. Its only real gap is guidance on when this feed is preferred over the other read tools, which the description does not address.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-arg tool applies.

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?

States a specific verb and resource ("Get a compact decision pack") and pins down its role as "the primary LLM feed." It characterizes content precisely (structured effects plus advisor output), but never names or contrasts the closest sibling, get_game_state, leaving the agent to infer why one would be chosen over the other.

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: "primary LLM feed" suggests this is the default read, but there is no explicit when-to-use, when-not-to-use, or comparison to get_game_state / run_turn / proceed. The reader must infer the routing.

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

Deploy Server

Other Tools