Lettras word search

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

With no annotations, the description carries the full burden and does well: it discloses that the tool is free with no usage limit, that the seed makes output reproducible, that native graphemes stay one cell, and that unused words land in an `unplaced` list. It does not explicitly state the absence of side effects or any failure mode when too few words fit, which keeps 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?

It is a single dense paragraph, front-loaded with the core purpose and the output structure before the peripheral notes. Every clause is informative, though the long inline explanation of the puzzle-marker conventions is heavy to parse and could be tightened.

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?

There is no output schema, so the description must describe the return value, and it does so thoroughly: both matrices, the marker semantics for blank/number/letter cells, the placements and unplaced lists. Combined with 100% schema coverage on inputs, an agent has everything needed to call and interpret the tool.

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 8 parameters, making 3 the baseline. The description reinforces a few semantics (seed reproducibility, entries as over-supply, native-letter handling) but largely repeats what the schema and title already convey, adding limited meaning beyond the structured fields.

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 opens with a specific verb+resource ("Lays out a crossword from words with clues") and immediately clarifies the return shape. It is clearly distinct from the word-search/sudoku siblings in spirit, though it never explicitly names or contrasts an alternative. Clear and unambiguous, but no sibling differentiation.

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 practical input guidance ("Offer more words than you need: the engine keeps the best layout it finds") and notes determinism via seed, which implies how to use the tool. However, there is no explicit when-to-use / when-not-to-use statement and no reference to the sibling generation tools to help an agent choose between them.

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 burden and does disclose non-obvious behavior: letters are sampled by language frequency, accents are honored per language, and the words argument prevents the filler from accidentally spelling a hidden word. It is silent on determinism (seed semantics live only in the schema) and on permissions/side effects, minor gaps for a pure computation 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?

Three tight sentences with no filler; the input contract and the words-safety rationale are front-loaded. Everything stated earns its place.

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 6-parameter tool with no output schema, the description gives enough to call it correctly (source of the grid, language/accent behavior, word-safety). It could say explicitly that a fully populated grid is returned, but that is easily inferred from 'completes' and 'fills them'.

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% and the schema descriptions already explain grid format, lang default, seed repeatability, the empty marker, and the words rationale. The description largely restates that ('pass the puzzle's words so the filler never creates an extra copy'), so the baseline of 3 is appropriate.

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 names a concrete verb and resource (fills the empty cells of a word-search grid), states the exact input contract (grid from generate_word_search, '-' marks empties), and names the sibling it pairs with. An agent can distinguish this filler from generate_word_search without opening a 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?

It clearly places the tool in a workflow: take the grid produced by generate_word_search and pass that puzzle's words along. It gives a usage condition (words are passed so no duplicate word is created) but never states explicit exclusions or alternatives for when not to call it.

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 load and does it well: it discloses the free/paid quota boundary, that native letters occupy a single cell, the exact output shape (grid plus words that did not fit), the '-' empty-cell convention, and seed determinism. It leaves error behavior when words cannot be placed and any auth requirements unstated.

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 leads, then constraints, then output and the handoff to fill_word_search. Five sentences, each carrying distinct information — quota terms, character handling, return contents, empty-cell marker, determinism — with no filler.

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?

There is no output schema and no annotations, so the description must explain returns and constraints itself; it covers grid contents, unplaced words, empty-cell marking, determinism, and the quota model. An agent has everything needed to call it and to chain it with fill_word_search.

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 rows, cols, seed, lang, position, difficulty, clustering, and classicMode are already documented; the description's statements about seed reproducibility largely restate the schema. It adds only marginal parameter meaning, such as native letters occupying one cell, so the 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 and resource — 'Create a word-search puzzle (sopa de letras) from a list of words' — and names the sibling that continues the workflow (fill_word_search), so an agent can separate it from crossword/sudoku without opening a 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?

Explains the quota condition that governs when the tool is usable ('up to 10x10 with at most 10 words are free and unlimited; bigger ones use the free puzzles each client gets') and explicitly routes follow-up work to fill_word_search. It never states when to prefer this over the sibling puzzle generators, so it stops short of full alternative coverage.

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, yet it discloses nothing about read-only safety, idempotency, caching, or response shape. The zero-argument nature implies a harmless static lookup, but that is inferred rather than stated.

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 front-loaded sentence with no filler; every word describes the returned content. The phrasing ('Languages Lettras supports') is slightly elliptical but still economical.

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 partially compensates by describing what comes back: languages plus the native letters they contribute. That is adequate for a trivial no-arg enumeration, though it omits the concrete return format (e.g., whether letters come as an array or string).

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?

The tool takes no parameters, which sets the baseline at 4. There is no parameter semantics to explain, and the description correctly does not invent any.

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 names the resource (supported languages) and even specifies the payload detail — the native letters each language adds to its grid alphabet. It's a noun phrase rather than a verb+resource, and it doesn't differentiate itself from the analogous sibling list_styles, so it falls short of a 5.

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 guidance on when to call this tool, no prerequisites, and no mention of alternative ways to discover languages or styles. The agent is left to infer that this is a discovery helper rather than being told.

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 disclosure burden, and it does disclose the shape of each returned entry (id, English name, category) plus the full category enum. It does not mention ordering, stability, or that no side effects occur, but for a zero-parameter read-only catalog this is solid.

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 sentence with no filler, front-loading the resource and then the item shape. The embedded list of categories makes the clause slightly dense 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?

There is no output schema, so the description correctly compensates by describing the returned fields and the category set. That covers what an agent needs to decide to call it and interpret results; only minor details like result ordering are absent.

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?

The tool takes zero parameters, so there are no parameter semantics to explain; baseline 4 applies. The description's mention of 'the id to pass' usefully clarifies how this output relates to another tool's parameters.

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 names the specific resource (the Unicode text styles offered by stylize_text) and ties it to the sibling tool that consumes the output, so an agent can tell it apart from crossword/sudoku/style siblings. It stops just short of an explicit verb phrase, but the scope is unambiguous.

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 phrase 'the id to pass' implicitly signals this is a lookup step whose output feeds stylize_text, which is the main when-to-use cue. However, there is no explicit statement of when to call this versus alternatives or any prerequisite/ordering 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?

With no annotations, the description carries the full burden. It usefully discloses accepted sizes (4, 6, 9, 12, 16), that 0 marks empty cells, that uniqueness is reported, and that there is no cost/rate limit. It omits behavior for unsolvable, malformed, or contradictory grids and does not describe the returned solution format.

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 tight sentence front-loads the operation, then parenthetically supplies the cell convention, then the uniqueness behavior and cost note. No filler.

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 one-parameter tool with no output schema, the description covers input conventions, supported sizes, and part of the return semantics (solution + uniqueness). Error/edge-case behavior remains unexplained, which is the main residual gap.

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, but the description adds meaning the schema does not: the schema permits any row/column length from 4 to 16, while the description narrows valid sizes to 4, 6, 9, 12, 16. That constraint is directly useful for constructing a valid puzzle.

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 ('solves a sudoku grid') plus the extra behavior of reporting solution uniqueness, which is more than the title conveys. It does not explicitly distinguish itself from the sibling 'sudoku', which the agent must infer is the generation counterpart.

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: supply a puzzle, get a solution. The 'free, no usage limit' note is useful context but there is no explicit when-to-use/when-not guidance or reference to the sibling sudoku tool for generating instead of solving.

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 burden and does so richly: accents are preserved via combining marks, some characters stay plain, those are surfaced in notNative and per-part missing flags, and it runs locally with no usage limit. An agent knows the output shape and limitations before calling.

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?

Front-loaded with purpose, followed by caveats, output hints, the classic flag, and invocation guidance. Sentences are dense and occasionally long, but nearly every clause carries information an agent needs; little 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?

There is no output schema, so the description compensates by describing the return structure and its edge-case fields (notNative, missing: true). Combined with invocation and character-coverage caveats, nothing needed to call it 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, but the description adds real meaning beyond the schema: omitting style means all 22, classic strips accents first (ñ becomes n), and it explains result fields (notNative, missing) the schema never mentions.

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 and resource ('Turns text into Unicode styles'), enumerates the style families, and gives the count (22 styles). It also routes the agent to the sibling list_styles for ids, so it is distinguishable from siblings without opening a 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?

Explains exactly how to invoke: pass one style id, several, or none for all styles, and points at list_styles for valid ids. It gives the condition for classic (fonts that render combining accents badly), but there is no alternative styling tool to compare against, so no explicit when-not-to-use.

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 burden and does reasonably well: it discloses the return shape ('puzzle (0 = empty cell) and solution'), a uniqueness guarantee, determinism ('the same seed gives the same sudoku'), and cost/limits ('Free, no usage limit'). It omits repr details for non-9 grids, but the behavioral profile is solidly covered.

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 purpose then return value then parameter/behavior notes. Every clause carries information and there is no filler.

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 zero-required-parameter generator with no output schema, the description covers what it does, what it returns, the valid parameter ranges and defaults, cost, and determinism. Nothing material an agent needs in order to call it 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 description coverage is already 100%, so the baseline is 3, but the description adds genuine meaning: it spells out the allowed sizes, expands difficulty as '1 easy (most clues) to 4 expert', and clarifies the seed as producing a repeatable puzzle. That exceeds what the schema alone conveys.

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 and resource ('Creates a sudoku with exactly one solution') and immediately names the return payload. The uniqueness guarantee makes it unambiguous that this is a generator, cleanly separating it from the sibling solve_sudoku.

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 implies when to reach for it (you want a new puzzle with a given size/difficulty/seed) but never names an alternative or a condition for choosing one. There is no explicit when-not guidance relative to solve_sudoku or the word-search siblings.

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