Zadely AI Browser Games

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

Annotations already cover read-only, idempotent, non-destructive and closed-world, so the bar is low; the description still adds real signal by enumerating returned content and, more usefully, disclosing failure behavior ('unknown slugs and classic ports return an error and no play link'). It stops short of any auth/quota context but that is minor for a public read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences; the lookup key and its derivation come first, then return payload, then error semantics. No filler, no restatement of the title.

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

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

With no output schema, the description carries the return-value burden and does so by listing the fields returned plus the error/no-play-link case. Nothing an agent needs to call this single-parameter tool correctly is missing.

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

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

Schema coverage is 100%, so the baseline is 3, and the description earns above baseline by defining the slug semantically as 'the last segment of https://zadely.com/games/<slug>/', which tells the agent how to derive it rather than only how to format it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('look up') plus resource ('one AI-made browser game') and the exact lookup key ('by its Zadely slug'). The word 'one' plus slug-keyed lookup implicitly separates it from the sibling list/search/random tools, so an agent can route correctly without opening another schema.

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

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

Usage is implied rather than stated: the tool is for when you already have an exact slug, and the note that 'unknown slugs... return an error' warns the agent that fuzzy or partial input will fail. However, it never names an alternative ('use search_games if you only have a name'), so the boundary with search_games must be inferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds genuinely useful context that annotations cannot: it enumerates the returned fields (award, credited AI tools, engine, pageUrl, playUrl), which is valuable given there is no output schema. It omits pagination and rate-limit behavior, keeping it short of a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences: purpose first, filter usage second, return shape third. Every sentence earns its place with no redundancy or filler, and the most important scoping information is front-loaded.

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

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

For a two-parameter, read-only list tool with full schema coverage and annotations covering the safety profile, the description closes the remaining gap by explaining the return fields in the absence of an output schema. An agent has everything needed to call and interpret it correctly.

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

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

Schema coverage is 100%, so both parameters (jam, limit) are already fully documented, establishing a baseline of 3. The description supplies example jam-name formats, but the schema itself already states that "vibe-jam-2026" and "Vibe Jam 2026" both work, making the added value marginal rather than additive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Starts with a specific verb (List) and a precisely scoped resource (AI-made jam winners and category awardees), naming concrete sources like Vibe Jam and the AI Browser Game Jam. This resource is plainly distinct from siblings such as list_trending, search_games, and random_game, so an agent can route to it without ambiguity.

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

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

The description explains how to use the jam filter ("Filter with a jam name such as..."), which implies the intended invocation context. However, it never says when to prefer this tool over search_games or list_trending, nor states any exclusions or when-not-to-use conditions, leaving the routing decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, non-destructive), so the description's job is to add context — and it does: result ordering, deliberate omission of classic ports versus the public trending page, and the shape of each result. It stops short of describing pagination or the exact size of the list, which is what keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the purpose and ordering, then the exclusion caveat, then the result fields. No filler and nothing buried.

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

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

With no output schema and no return-value documentation elsewhere, the description carries the burden and does well by naming the fields returned (pageUrl, playUrl, credited AI tools, engine). The main remaining gap is the size/scope of a default (unlimited) response, which an agent might want before calling.

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

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

There is a single parameter at 100% schema description coverage, including the max of 50 and the 'omit for full list' default, so the schema fully documents it. The description adds nothing about `limit`, which is the expected baseline when the schema does the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb (list), a precise resource (AI-made browser games Zadely marks as trending), and a defined scope and ordering ('most in demand first'). It is easy to separate from siblings like list_jam_winners and search_games, which cover different result sets.

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

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

Usage is implied rather than stated: an agent infers this is the browse-the-trending-feed tool. The exclusion of classic ports is helpful scope clarification, but the description never contrasts itself with search_games or random_game, so the agent gets no explicit routing guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Beyond the annotations, the description discloses the returned draft fields, explicitly states that no email is sent and no submission or listing is created, and explains that the submitter must send the draft or authorize an email tool. It also adds a safety instruction to treat draft contents as untrusted submission data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded in the first sentence, and the remaining sentences add distinct and useful information about inputs, return fields, side-effect boundaries, sending responsibility, and trust handling. There is no filler or repetition.

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

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

For an 18-parameter tool with no output schema, the description explains the draft return shape and the critical non-actions. Combined with the rich schema and annotations, it gives an agent enough context to call the tool correctly and understand that the output is an unsent, untrusted draft.

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

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

Schema description coverage is 100%, so the schema already documents all 18 parameters in detail, including validation constraints and enum meanings. The description only summarizes categories such as game, gameplay, controls, creator, and contact details, adding little parameter-specific meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: prepare a complete email draft to submit a browser game to Zadely for human editorial review. It clearly distinguishes the tool from sibling read/list/search tools by focusing on submission preparation, and explicitly says it does not create a listing.

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

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

It gives a clear usage context: submitting a browser game for human editorial review, with verified details. It also clarifies key boundaries by saying it does not send email, save a submission, or create a listing, but it does not name alternative tools or give explicit when-not-to-use guidance beyond those boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already cover the safety profile (readOnlyHint=true, destructiveHint=false, idempotentHint=false), and the description adds value on top by enumerating the returned payload, which is important since there is no output schema. It does not mention what happens when filters match nothing, which is the main residual gap for a random-selection tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, zero filler, with the core action front-loaded before the filter and return-value details. Every clause carries information the agent needs.

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

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

The description covers action, filtering semantics, and return fields, and annotations carry the safety profile, so an agent can call it correctly. Only empty-result behavior and pagination-style concerns are unaddressed, which are minor for a single-item random pick.

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

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

Schema coverage is 100%, so the schema already documents each filter's type and examples, making 3 the baseline. The description adds cross-tool semantics the schema cannot convey: these filters use the same matching rules as search_games, so an agent can reuse filter values it already knows work there.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ("Pick one AI-made browser game at random"), and the singular-random framing separates it cleanly from search_games and list_trending without needing to open the schema. It also names the fields returned (pageUrl, playUrl, AI tools, engine), so the agent knows exactly what it gets.

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

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

It gives clear context for invocation by noting the optional filters exist and that they follow search_games matching rules, which implicitly routes list-style queries to that sibling. It stops short of an explicit when-not-to-use statement or naming search_games as the direct alternative for multi-result needs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds genuinely new behavioral context: each result carries pageUrl (with an explicit citation instruction) and playUrl, plus credited AI tools and engine — important given there is no output schema. Ordering, pagination, and empty-result behavior remain undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with purpose and facet coverage, followed by scope exclusion and return-shape note. No filler or repetition.

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

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

With no output schema, the description usefully covers the key return fields and citation guidance, and the 7 fully documented params need no narrative support. Missing only secondary details such as default result count, ordering, and no-match behavior.

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

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

Schema description coverage is 100%, and each parameter already documents its format (slugs, partial AI-tool names, booleans). The description's facet list largely restates the schema's fields, adding no syntax or matching semantics beyond what the schema provides; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (search) plus resource (Zadely's AI-made browser games) and enumerates the exact facets it searches on, matching the parameter list. The exclusion of classic commercial ports further sharpens scope against the broader game-catalog siblings.

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

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

Gives clear context for when this tool applies (faceted discovery of AI-made browser games) and a negative scope rule that classic ports are not searchable. It does not explicitly name when to use get_game, random_game, or list_trending instead, so routing among siblings is left partly to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.