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Get personalized game recommendations

get_recommended_games
Read-only

Infers your game preferences from your Steam library's most-played tags to recommend unowned games, weighted by playtime and review scores.

Instructions

Recommend unowned Steam catalog games personalized to this player: tags on their own most-played owned games become weighted preferences (more playtime on a tag = more weight), discounted by each candidate's review score so a tag match on a poorly-received game doesn't outrank a better one, then ranked against a broad catalog page, excluding anything already owned. based_on_tags shows which of the player's own top tags drove the ranking; each result carries matched_tags and match_score alongside the usual price/review/compat card. Set exclude_tags to steer away from a genre despite it matching by playtime (e.g. 'recommend me something except Souls-like'), or min_discount for 'recommend something on a good discount' (e.g. 'suggest games on sale, not RPGs or shooters' → exclude_tags:['RPG','Shooter','FPS'], min_discount:30). Different from discover_games (which needs YOU to name the filters) — this infers taste from the player's WHOLE library instead, for 'what should I play next' / 'recommend me something'. For 'something like THIS ONE game' (a single named title), get its tags via get_items and call discover_games with them instead. Note: candidates are scored from a large but fixed-size catalog scan, so a heavy exclude_tags/min_discount combination can return fewer than count — there's no larger scan to fall back to. Requires STEAM_API_KEY and a public profile with game-details visible (same requirement as get_owned_games) — found:false is also returned if the player owns no games at all to base recommendations on.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNoHow many recommendations to return (1-25). Default 10.
steamidNo17-digit SteamID64. Omit to use the STEAM_ID configured on the server. Convert a vanity/custom URL name with resolve_vanity_url first.
exclude_tagsNoDrop any candidate carrying ANY of these tags (case-insensitive), e.g. ['Souls-like'] for 'recommend me something except Souls-like'. Use exact Steam tag names — a misspelled/unrecognized one isn't an error, it just drops nothing.
min_discountNoMinimum discount %, e.g. 30 for '30%+ off'. Omit to include full-price games too.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations already mark readOnlyHint and openWorldHint. The description adds significant behavioral context: the algorithm weighs tags by playtime and discounts by review score, limitations on catalog scan size, and the 'found:false' return when no games owned. No contradiction with annotations.

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 thorough and well-structured with examples and comparisons. It is longer than necessary but each sentence adds value. Some redundancy could be trimmed (e.g., the 'found:false' mention might be placed more concisely), but overall it's appropriately detailed for a complex tool.

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?

Given the tool has 4 parameters, output schema present, and no nested objects, the description covers all essential aspects: usage, algorithm, limitations, prerequisites, and error conditions. It leaves no major gaps for an AI agent to guess.

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

Parameters5/5

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

Schema description coverage is 100%, so baseline is 3. The description adds valuable context beyond the schema: explains how exclude_tags works with examples ('recommend me something except Souls-like'), how min_discount is used, and the interaction between count and catalog limits.

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 starts with 'Recommend unowned Steam catalog games personalized to this player' which clearly states the action and resource. It distinguishes from sibling discover_games by explaining that this tool infers taste from the player's library rather than requiring manual filters.

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool ('what should I play next' / 'recommend me something') and when to use alternatives (for a single named title, use discover_games). It also provides examples of exclude_tags and min_discount usage, and notes prerequisites like STEAM_API_KEY and public profile.

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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