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List contest problems

repovive_list_contest_problems
Read-only

Lists all problems in a contest with metadata like difficulty, points, tags, and solve counts. Optionally includes full statements; hidden test cases are never returned.

Instructions

List every problem in one contest with its slug, order, difficulty, points, limits, tags and solve counts. Set full=True to also include statements. Hidden test cases are never returned. Get contest ids from repovive_list_contests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fullNoInclude full statements (description, constraints, I/O formats). Default: summaries only.
contest_idYesContest ObjectId (24 hex chars) from repovive_list_contests
response_formatNo'markdown' or 'json'markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare the tool read-only, and the description adds meaningful behavioral context: hidden test cases are never returned and full=True controls whether statements are included. This goes beyond the structured annotations to set expectations about output limits and privacy.

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?

Three sentences, each earning its place: the first defines the tool's output, the second explains an optional flag's effect and a critical exclusion, and the third provides the dependency for obtaining a valid contest_id. The key scoping is front-loaded.

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

Completeness5/5

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

The description is complete for a read-only listing tool: it names returned fields, explains the one optional behavior, discloses that hidden tests are excluded, and points to the correct source for contest IDs. With an output schema present and annotations covering safety, nothing needed to invoke it correctly is missing.

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 input schema already documents contest_id, full, and response_format. The description adds a useful behavioral note about full=True including statements, but does not need to repeat parameter meanings; the schema covers the basics.

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 states a specific action ('List every problem in one contest') and enumerates the exact fields returned: slug, order, difficulty, points, limits, tags, and solve counts. This clearly distinguishes it from sibling tools like listing contests or retrieving 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 Guidelines4/5

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

The description provides clear context for when to use this tool: to enumerate problems within a contest. It also gives an important prerequisite by directing the agent to get contest ids from repovive_list_contests. It does not explicitly state when to prefer alternatives, but the usage context is unambiguous.

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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