api-schema-drift-detector

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

No annotations are provided, so the description carries full burden. It lists what changes are detected, providing reasonable transparency for a read-only analysis tool. However, it does not describe how the analysis works (e.g., schema format assumptions, whether it handles nested objects) or any side effects, rate limits, or auth needs.

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 one sentence followed by a bulleted list, front-loaded with the main purpose. Every sentence is necessary and concise with no wasted words.

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 no output schema, the description fails to explain the format of the result (e.g., a report, list of changes). This is a significant gap for an analysis tool. Additionally, with siblings present, the description does not differentiate when to use each tool, reducing completeness.

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% with both parameters described in the schema ('New (candidate) schema version' and 'Previous (deployed) schema version'). The description adds no additional meaning beyond these descriptions, so 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 clearly states the tool's purpose: 'Analyzes breaking changes between two schema versions.' It lists specific change types (field removal, type changes, etc.), distinguishing it from sibling tools like drift-detector and schema-validator.

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 usage for comparing schema versions to detect breaking changes, but does not explicitly specify when to use this tool over siblings (drift-detector, schema-validator) or provide exclusions.

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?

The description discloses key behavioral traits: it reports five specific diff types with severity (error/warning/info) and recurses into nested objects. With no annotations, it carries the full transparency burden; it covers what outputs to expect, though it does not mention error handling for invalid inputs.

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 a single sentence that front-loads the main purpose ('Detects differences...') followed by bullet-like details. It is efficient, though it could be split for readability. No unnecessary words, but the list format in prose is slightly dense.

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 tool with no output schema, the description adequately explains the output structure (list of differences with types and severity) and behavior (recursion). It covers the core functionality, but misses any mention of constraints on the schema input (e.g., must be a valid JSON Schema). Overall, it is sufficient given the tool's simplicity.

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?

Both schema and response parameters are described in the input schema ('Expected JSON schema' and 'Actual API response to check against schema'). The description does not add new meaning beyond the schema, but it contextualizes that both are required and how they are used together. Given 100% schema coverage, a baseline score 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 clearly states it detects differences between an actual API response and an expected JSON schema, listing specific difference types and severity levels. This distinguishes it from sibling tools like 'schema-validator' (validation only) and 'breaking-change-analyzer' (version change analysis), making the 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 implies usage for checking API response against a schema, but does not explicitly state when to use this tool versus alternatives (e.g., schema-validator for strict validation, breaking-change-analyzer for version differences). No exclusion or context cues are provided, leaving the agent to infer.

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 full burden. It discloses the return format (valid true/false and error message), but does not mention edge cases, performance, or error handling beyond the basic output. Adequate but minimal.

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 a single sentence that immediately specifies the tool's action and output. Every word serves a purpose, with 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 a simple validation tool with 2 parameters and no output schema, the description is fully sufficient. It covers input, action, and output format, leaving no critical gaps.

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 baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions; it merely reiterates that data is any type and schema is a JSON schema. No enrichment.

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 clearly states the verb 'validates' and the resource 'data conforms to a JSON schema', differentiating it from siblings like 'breaking-change-analyzer' and 'drift-detector' which analyze changes rather than initial validation.

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 explicit guidance on when to use or when not to use this tool versus alternatives. The description implies use for validation, but lacks contextual boundaries or exclusion criteria.

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