luogu-mcp-server

Find Related Luogu Problems

luogu_find_related_problems

Read-only

Discover Luogu practice problems related to a topic, pain point, or search query to target areas for improvement.

Instructions

Find related Luogu practice problems by mixing topic/pain-point recommendations with live keyword search.

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": "luogu_find_related_problems"
  }
}

Input Schema

Name	Required	Description
`query`	No	Free-text query, title fragment, or topic phrase.
`pid`	No	Current Luogu problem id, used for context and exclusion in future versions.
`topic`	No	Topic such as binary_tree, recursion, matrix, output_format, tree_distance.
`painPoint`	No	Student pain point such as traversal_order_confusion or sentinel_input.
`currentProblemId`	No	Exclude this current Luogu problem id.
`limit`	No	Maximum returned related problems.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.0May 28, 2026

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the description adds context by explaining the mechanism ('mixing… recommendations with live keyword search'). However, it does not disclose important behaviors like how results are returned, whether exclusion via currentProblemId is fully implemented (described as 'future versions'), or any potential limitations.

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?

The description is a single, concise sentence that efficiently communicates the tool's purpose. It is front-loaded with the core functionality. However, it could be slightly improved with structured guidance on how parameters interact, making it more scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of six optional parameters and no output schema, the description leaves significant gaps. It does not explain how multiple parameters combine (e.g., query + topic), what the response format is, or provide examples. Annotations cover safety but not interaction semantics. More detail is needed for effective agent use.

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?

The input schema has 100% description coverage, so the baseline is 3. The description adds marginal value by clarifying the query parameter ('Free-text query, title fragment, or topic phrase') and noting that pid is for future use. Most parameter meanings are already clear from 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 uses a specific verb ('Find'), identifies the resource ('related Luogu practice problems'), and explains the method ('mixing topic/pain-point recommendations with live keyword search'). It effectively distinguishes the tool from siblings like luogu_find_topic_problems or luogu_search_problems by combining both approaches.

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 implies usage when a blend of topic/pain-point recommendations and keyword search is needed, but it lacks explicit guidance on when not to use this tool or how it compares to alternatives such as luogu_recommend_problems or luogu_search_problems. No exclusions or conditions are 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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