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DolfDarts Dart Game Encyclopedia

Recommend dart games

recommend_games
Read-only

Recommend a ranked shortlist of dart games for a given play context (player count, optional difficulty, time budget, and board type). Each recommendation includes a reason, the board it needs (boardType, boardLabel, boardUrl) and its dolfdarts.com URL. Most players have the standard board; when boardType is omitted, standard-board games are ranked first.

Input Schema

NameRequiredDescriptionDefault
limitNoMax recommendations (default 5).
boardTypeNoThe dartboard the players have. Most players have 'standard'; omit to include all 38 board types. Board ids and live game counts: browse_taxonomy (boards). When omitted, standard-board games are ranked first.
difficultyNoPreferred difficulty.
maxMinutesNoTime budget in minutes.
playerCountYesNumber of players who will play (required).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered; the description adds genuine value by disclosing the return shape (a reason per item plus boardType, boardLabel, boardUrl and a dolfdarts.com URL) and the ranking rule. It does not mention the default result count, which the schema covers, or any ordering caveats beyond the standard-board rule.

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?

Three sentences, front-loaded with what the tool does, then what comes back, then the one non-obvious ranking rule. The middle sentence is dense but every clause is informative; minor overlap with schema text on boardType.

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?

With no output schema, the description steps in to describe the returned fields and the ranking logic, which is the critical missing piece. For a read-only 5-parameter recommender this is nearly complete; only the default limit and any tie-breaking beyond board type are left unstated.

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 coverage is 100% and the schema already documents limit defaults, the 38 board ids, and the 'omit boardType' ranking behavior, so the description is largely restating structured data. It adds only a soft hint ('most players have the standard board') that is also present in the schema.

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?

States a specific verb ('Recommend a ranked shortlist') and resource ('dart games') scoped to a play context. The ranking/personalization framing clearly separates it from the retrieval siblings (list_games, search_games, get_game) without needing to name them.

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 implied by the play-context framing (player count, difficulty, time budget, board), but there is no explicit 'use this when' or 'use search_games instead for X' guidance. The one routing hint given ('when boardType is omitted, standard-board games are ranked first') is about parameter behavior, not tool selection.

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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