check_solutionCheck a grid against a puzzle's clues, using the same rule as the benchmark grader. Reports which rows and columns do not match.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Puzzle id from list_puzzles | |
| grid | Yes | Row-major string of 0 (empty) and 1 (filled), width × height characters |
| Name | Required | Description | Default |
|---|---|---|---|
| error | No | ||
| correct | Yes | ||
| rowViolations | Yes | ||
| columnViolations | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "https://json-schema.org/draft/2020-12/schema",
+ "additionalProperties": {},
+ "properties": {
+ "columnViolations": {
+ "items": {},
+ "type": "array"
+ },
+ "correct": {
+ "type": "boolean"
+ },
+ "error": {
+ "type": "string"
+ },
+ "rowViolations": {
+ "items": {},
+ "type": "array"
+ }
+ },
+ "required": [
+ "correct",
+ "rowViolations",
+ "columnViolations"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds meaningful context beyond that: it uses 'the same rule as the benchmark grader' and reports specific mismatched rows and columns, which helps set expectations for the check 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?
Two sentences, front-loaded with the tool's purpose and followed by the reporting behavior. Every sentence earns its place with no redundancy.
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 low-complexity read-only check tool, the description covers purpose and key behavior, while the schema documents parameters and an output schema exists for return values. It is nearly complete, though usage context relative to sibling tools is 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?
Schema description coverage is 100%, and the two parameters (id and grid) are fully documented in the schema, including the grid's row-major 0/1 format. The description adds no parameter-level detail beyond the schema, so baseline 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 states a specific verb and resource: 'Check a grid against a puzzle's clues' and clarifies it uses the benchmark grader's rule. It does not explicitly distinguish itself from siblings such as get_puzzle_results, so it is clear but lacks 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?
The description says what the tool does but gives no guidance on when to use it versus alternatives like get_puzzle_results or get_puzzle. There are no prerequisites, exclusions, or alternative tools named.
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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