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vic3-mcp

vic3-mcp

by kaisonlau8
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_save

Search the raw text of a processed Victoria 3 save to find specific fields for debugging or examining rare data.

Instructions

在融化后的存档文本里搜索原始字段（调试/查冷门数据）。

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": {
      "query": "<query>"
    },
    "name": "search_save"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
max_hitsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It communicates that the search operates on melted save text and targets raw fields, which is core behavior. However, it does not clarify match semantics, whether it searches field names or values, or any limits beyond the schema default.

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, focused sentence that front-loads the key scoping ('melted save text', 'raw fields') and the intended use cases. Every part adds value and there is no 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 simple 2-parameter tool with an output schema, the description covers the core purpose and typical usage context. However, with no annotations and no parameter-level description, the agent still lacks some detail about query behavior and hit limits.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description needed to compensate. It implicitly suggests that query is a search term for raw fields, but it never mentions max_hits or clarifies query syntax/format. This leaves parameter semantics mostly undocumented.

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?

The description states a specific verb and resource: searching raw/original fields within the melted save text, with explicit use cases (debugging/looking up obscure data). It does not explicitly contrast with sibling tools, but 'raw fields' clearly distinguishes it from higher-level analysis tools like overview or analyze_situation.

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

Usage Guidelines4/5

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

The parenthetical '(调试/查冷门数据)' provides clear context for when this tool is appropriate: low-level debugging or uncommon data lookups. It does not explicitly name alternatives or state when not to use it, so it falls short of a 5.

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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