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

No annotations, so the description carries the full burden. It usefully discloses a non-obvious behavioral trait — the pattern inserts a hidden 0.01° midspin — which an agent could not infer from the name. However it says nothing about whether the file is written in place, whether it requires a validated level, or what is returned.

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?

One scoping sentence followed by a compact Args block that maps each parameter to its meaning. Front-loaded and free of filler, though the Korean-only text may add a reading cost for non-Korean agents.

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?

A mutation tool with no annotations and no output schema needs to state side effects and result shape, which this description omits. It covers parameters and one behavioral quirk but leaves file-write semantics, error cases, and return value unaddressed.

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 0%, so the description must compensate, and it does: level_path is an absolute .adofai path, floor is the target tile index, and keys enumerates the valid values (2 = both hands / 3 / 4). This adds meaning beyond the bare integer/string types in the schema, though the default (keys=2) is not restated.

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 action (add a 2/3/4-key simultaneous-press pattern) against a specific resource (an ADOFAI level, at a given floor). The parenthetical '(0.01° hidden midspin)' specifies the exact pattern variant, so the purpose is concrete. It does not differentiate itself from siblings like generate_full_map or adjust_effects, which is the only gap.

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?

No guidance on when to choose this over siblings such as generate_full_map or adjust_effects, and no prerequisites or ordering constraints (e.g., must the level be loaded/validated first). Usage must be inferred entirely from the name.

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?

With no annotations, the description carries the full behavioral burden. It usefully discloses what is NOT destroyed (tile length and rhythm preserved), which is meaningful for a regeneration tool, but it does not state whether the .adofai file is overwritten in place, whether a backup is made, or any permission/auth requirements.

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?

Purpose is front-loaded in the first line, followed by a compact Args block; there is no padding. Slight terseness in entries like '기준 카메라 줌' costs it a perfect score but nothing is wasted.

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?

Parameters are well covered, but for a mutation tool with no annotations and no output schema, the definition omits file-write/overwrite behavior and any failure or safety context. It is adequate but leaves real gaps an agent would want.

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 0%, so the description is the only source of parameter meaning, and it delivers: level_path is clarified as an .adofai absolute path, style enumerates valid values (showcase/clean/party) that the schema lacks, and zoom/enable_flash are explained. This substantially compensates for the coverage gap, though units/defaults are still implicit.

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+resource (regenerate effects/visuals) and a distinguishing scope constraint (preserves tile length and rhythm), which separates it from the sibling generate_full_map. It does not name siblings explicitly, but the 'only effects' scoping makes the boundary inferable.

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 '만' (only) qualifier and the preservation clause imply when to use it: when effects must change but the chart layout/rhythm must stay intact. However, no alternative tool is named and no explicit when-not condition is given, so routing is left 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?

With no annotations, the description carries the full behavioral burden. It lists analysis outputs but omits whether the operation is read-only, what resources it consumes, error conditions, or side effects, which is a notable gap for an audio-processing 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?

The description is front-loaded and compact: first the tool purpose with outputs, then the parameter note. Every sentence earns its place with no redundant restatement of the schema 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?

For a multi-output audio analysis tool with no output schema or annotations, the description covers purpose and path requirements. However, it omits sequencing with siblings such as generate_full_map and whether results are returned as data or written to disk, leaving clear gaps for an agent to infer.

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 0%, so the description must compensate for the single parameter. It specifies that the path is for a local audio file, must be absolute, and supports mp3/ogg/wav, which meaningfully supplements the bare string schema, though it does not cover file existence or size limits.

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?

It states a specific verb and resource: precise analysis of a song file, and enumerates the returned metrics (BPM, offset, beat confidence, onsets, musical sections). It does not differentiate itself from siblings like generate_full_map, but the core purpose is clear.

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?

No guidance is given on when to use this tool versus alternatives, prerequisites, or sequencing with sibling tools. The only contextual clue is that the path must be a local absolute path, which is parameter semantics rather than usage 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose that output_dir is created if missing ('없으면 생성'), which is useful. However, it never says whether existing .adofai files are overwritten, how long the generation takes, whether it needs network/GPU, or what the success output looks like — significant gaps for a heavyweight generation 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?

The headline sentence is front-loaded and the Args block is a tight per-parameter list with no filler. Structure is easy to scan, though the header line's marketing adjectives ('인간미 넘치는 유튜브급') spend a little space without adding selection value.

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 9-parameter generation tool with no annotations and no output schema, the description covers the inputs well but leaves behavioral essentials unaddressed: overwrite behavior, runtime/cost, and when to prefer this over the analyze/add_multipress/adjust_effects pipeline. Adequate for calling it, thin for calling 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 description coverage is 0% and there are 9 parameters, so the description must compensate — and it largely does, defining every argument including meaningful semantics the schema lacks: difficulty bands (입문 1~3 … 고수 9~10), the style presets (showcase/clean/party), and the focus modes (balanced/melody/beat). Only minor gaps remain (e.g. seed behavior beyond '다양성 시드', allowed style/focus values not exhaustive).

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 (생성/generate) and resource (풀맵/full map) and flags the one-shot nature with '(원샷)', which hints at how it differs from the granular siblings like add_multipress and adjust_effects. It stops short of explicitly naming those siblings as alternatives, so an agent must infer the routing.

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?

There is no when-to-use or when-not-to-use guidance. The only routing signal is '(원샷)', implying it bundles what the sibling tools do piecewise, but no condition, prerequisite, or alternative tool is named. An agent gets no explicit rule for choosing this over the pipeline of siblings.

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?

No annotations are provided. The description only says it lists items and does not disclose whether it returns a static or dynamic set, ordering, pagination, or any contextual constraints.

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?

A single, short sentence that is front-loaded and contains no wasted words. It is appropriately sized for a parameterless list operation.

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 parameters, no output schema, and no annotations, the description is minimally sufficient. However, it could clarify the return format or scope of 'available' to be more complete.

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 are zero parameters, so the baseline is 4. The description does not need to explain parameter semantics.

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 resource it lists (tile patterns and motifs) and that it lists available ones. It is clear what it does, though it does not differentiate from siblings, which are unrelated functions.

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 by the purpose—fetching available patterns—but there are no explicit when-to-use or when-not/alternatives stated. No alternatives exist among siblings that overlap, so more guidance is not strictly necessary.

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?

No annotations are provided, so the description carries the full behavioral burden. It says what is checked (loading safety + quality gate) but not whether the operation is read-only, what a failure looks like, or what criteria the quality gate applies. For a validation tool with zero annotation coverage, this is a meaningful gap.

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?

Very short and front-loaded: purpose first, then the single argument with its format constraint. Nothing is wasted, though the terse style leaves behavioral gaps that aren't a conciseness virtue.

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 simple one-parameter tool this is close to adequate, but no output schema exists and the description never indicates what validation returns (pass/fail, report, errors) or what the quality gate evaluates. A validation tool's result semantics matter to correct invocation.

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 0% and the schema only types level_path as a string with the title 'Level Path'. The description compensates by stating it must be an absolute path to a .adofai file, adding real constraint information the schema lacks.

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 (validate) and resource (.adofai file) with scope: loading safety plus quality gate. This clearly distinguishes it from the generative/analysis siblings (generate_full_map, analyze_song), though it doesn't explicitly name them.

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?

'생성된 .adofai 검증' implies usage after generation, which is a reasonable implicit cue. However, there is no explicit when/when-not guidance or mention of alternatives among the siblings.

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