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luogu-mcp-server

luogu-mcp-server

by Kaiserunix
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Fetch Luogu Training Set

luogu_fetch_problem_set
Read-only

Retrieve a Luogu training set by ID and get summaries of its problems.

Instructions

Fetch a Luogu training/problem set and its problem summaries by id.

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": "luogu_fetch_problem_set"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesLuogu training id, such as 100.
limitNoMaximum returned problem summaries.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4/5.0
Behavior4/5

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

The description states it returns 'problem summaries', which is behavioral context beyond the annotations (readOnlyHint, openWorldHint). No contradictions.

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 11-word sentence with no filler, efficiently conveying the purpose.

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 simple fetch tool with annotations and good schema coverage, the description adequately explains what it returns. However, lacking an output schema, some details about the return format would enhance 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?

Input schema has 100% coverage with clear descriptions for both parameters. The tool description adds no extra meaning beyond stating 'by id', which is already implied.

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 the action ('Fetch'), the resource ('a Luogu training/problem set and its problem summaries'), and the required identifier ('by id'). This distinguishes it from sibling tools like luogu_fetch_problem (which fetches a single problem).

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?

The description does not explicitly specify when to use this tool versus alternatives. It implies usage when the training set ID is known, but lacks guidance on when to prefer it over search or fetch individual problem tools.

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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