PlaySudoku

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

Annotations already mark readOnlyHint=true, and the description adds substantial behavior beyond that: it promises factual information only, disclaims hints/coaching, enumerates exactly what will be returned, and states every response includes a solve_url. This is rich behavioral context for an agent.

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?

Four sentences with no wasted text: the main purpose is front-loaded, followed by return behavior, the key exclusion/alternative, and a stable response-field guarantee. Every sentence contributes useful information for tool selection or invocation.

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, but the description compensates by enumerating the return facts (completeness, correct cells, incorrect cells with entered digit, empty count) and the guaranteed solve_url. It also gives concrete user utterances for when to call it, so an agent has all the information needed to invoke 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 100%, and both parameters (puzzle and progress) are already documented as 81-character strings with empty-cell conventions. The description does not need to re-explain parameters, so it stays at the baseline 3 because the schema carries that burden.

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 object: "Check a user's current progress against the correct solution." It further enumerates the exact factual outputs (completeness, correctness, incorrect cells, empty count) and explicitly contrasts with the sibling explain_next_step by saying it does not give hints or coaching, so an agent can distinguish it without opening schemas.

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 explicit trigger phrases: "Use this when the user asks 'am I done?' or 'did I make any mistakes?'" and names the alternative for hint requests: "Does not give hints or coaching — use explain_next_step for that." This covers both when to use and when not to use the tool.

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 only mark readOnlyHint=true, but the description adds substantial behavioral context: error detection returns incorrect_cells instead of a hint, two-board mode validates user entries, single-board mode cannot perform error checking, and every response includes solve_url plus tutorial_links on success. This goes far beyond the annotation.

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 long but tightly organized with mode headings and purposeful bullets. Every section earns its place, the core purpose is in the first sentence, and the return contract is stated compactly at the end.

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 properly discloses the return shape: success returns technique, explanation, action, and tutorial_links, while error mode returns incorrect_cells and message. It also covers the two invocation contexts and the always-present solve_url, so an agent has everything needed to call and interpret the tool 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?

Although the input schema already covers both parameters fully, the description enriches their meaning: puzzle is the original clue string in two-board mode but becomes the user's full board in single-board mode, and progress must preserve clue digits while omission yields the first hint. This is exactly the kind of mode-dependent semantic nuance that the schema alone cannot convey.

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 and resource: 'Get the next logical solving step for a sudoku puzzle.' It clearly distinguishes the tool's coaching role from siblings by explaining step-by-step hints, and it even separates two operational modes, making its purpose 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 description gives explicit when-to-use guidance for both modes: 'use when you know both the original clue grid and the user's current work' and 'use when the original clue grid is unknown.' It also states the coaching intent, but it does not explicitly name sibling tools like solve_sudoku or check_progress as alternatives, so the when-not-to-use guidance is slightly implicit.

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?

Even though readOnlyHint is true, the description adds substantial behavioral detail beyond annotations: the 81-character string format, 0-for-empty-cell encoding, included techniques and solve_url, difficulty givens ranges, technique-specific filtering, and error behavior when no puzzle matches. No contradiction with annotations exists.

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 well-organized and front-loaded, but it repeats the solve_url detail twice: 'response includes ... a solve_url' and later 'Every response includes a solve_url.' This redundancy makes it longer than necessary, though most sentences do add useful information.

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 fully compensates by explaining the return format, puzzle encoding, difficulty options, technique filtering, error behavior, and alternative tools. An agent has enough information to select parameters and interpret the response 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 the baseline is 3. The description adds meaningful context beyond the schema by explaining what the technique parameter does, when it is useful, and how errors behave if no matching puzzle exists. It also enriches the difficulty parameter with concrete givens ranges.

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 and resource: 'Get a random sudoku puzzle from the PlaySudoku.us collection.' It also differentiates the tool from siblings by explicitly directing users to solve_sudoku for full solutions and explain_next_step for hints.

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 provides clear guidance on when to use this tool versus alternatives: 'Use solve_sudoku for a full step-by-step solution, or explain_next_step to get one hint at a time.' It also gives a concrete use case for the optional technique parameter, such as practicing 'X-Wing' or 'Hidden Pair'.

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 readOnlyHint annotation, the description discloses key behavioral traits: it 'only uses logical techniques — never guesses' and that steps will 'end before completion' if the puzzle cannot be solved logically. It also explains the response includes every solving step, a technique summary, and a solve_url, giving the agent a clear model of call behavior.

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 organized into short labeled sections and front-loads the primary purpose before input and return details. It loses a point for minor redundancy: 'tutorial links' appears in the returns list and is then restated as 'tutorial_links' in the final sentence.

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?

Given a single parameter and no output schema, the description supplies all essential context: exact input format, return contents, logical-only behavior, early termination condition, and an explicit use case. It also mentions the interactive solve_url and tutorial links, which are valuable for an agent deciding to invoke 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?

The only parameter, puzzle, is already fully documented in the schema (81-character string, 0 or ., whitespace ignored), so the description merely repeats schema content. With 100% schema description coverage, the baseline of 3 is appropriate; the description adds no significant parameter meaning beyond what the schema already provides.

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 uses a specific verb ('solve') and resource ('a sudoku puzzle'), and specifies the manner: 'completely with step-by-step logical deduction.' It also states when to use it — 'when the user wants to understand how to solve a puzzle from scratch' — helping differentiate it from sibling tools like explain_next_step or get_sudoku_puzzle.

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 explicitly names its intended use case: 'Use this when the user wants to understand how to solve a puzzle from scratch.' However, it does not state when not to use it or explicitly mention alternatives such as explain_next_step for a single step or check_progress for tracking existing progress, so it falls slightly short of full alternative routing.

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