api-payload-auditor

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

No annotations provided, so description must disclose behavior. It only states the output (cost scenarios) but does not mention safety, idempotency, permissions, or any side effects. For a calculation tool this is minimal but acceptable; however, more detail on assumptions (e.g., default region) would improve transparency.

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?

Single sentence, front-loaded with key action, efficient and 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?

No output schema, and description only mentions 'returns cost at 25%, 50%, and 75% reduction scenarios' without specifying cost units, formula, or impact of region and monthlyRequests. For a tool with three parameters, more detail is needed for complete understanding.

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 parameters are documented structurally. Description adds meaning by naming 'payload size and monthly traffic' but does not elaborate on the 'region' parameter or the default for 'monthlyRequests'. Baseline of 3 is appropriate as description adds limited extra 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?

Description clearly states the verb 'Calculate', the resource 'cloud API egress bandwidth costs', and specific inputs (payload size, monthly traffic) and outputs (cost at three reduction scenarios). It is distinct from siblings like 'compare_payloads' which suggests comparison, not calculation.

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 use this tool versus alternatives. With siblings like 'field_prune_suggest' and 'payload_analyze', the description does not help differentiate usage contexts.

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 clearly discloses the return values (size reduction %, removed fields, depth change). It is transparent about the comparison behavior, though it could mention constraints like requiring similar structure or performance implications.

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, front-loaded sentence that efficiently conveys the tool's purpose and outputs without any 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?

Despite lacking an output schema, the description lists the three key return values. For a tool with two required parameters and no complex outputs, this is sufficient, though mentioning expected payload structure could improve 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 coverage is 100%, and the parameter descriptions in the schema are clear. The tool description does not add significant additional meaning beyond confirming the roles of original and optimized payloads, 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 clearly states the tool compares an original and optimized payload, specifying the exact outputs: size reduction %, removed field paths, and depth change. This distinguishes it from sibling tools like bandwidth_cost_calc or field_prune_suggest.

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 payload versions but provides no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites.

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 that the tool flags specific field types and uses a threshold, but does not mention the output format or whether it modifies the payload. The non-destructive nature is implied but not explicit.

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, zero wasted words. The first sentence provides the core purpose, and the second adds specific flag criteria. Highly efficient.

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?

The description explains what the tool checks but does not describe the output format (e.g., list of field paths with scores). Since there is no output schema, this omission reduces completeness. Threshold behavior is mentioned but not fully detailed.

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 parameters are fully described in the schema (100% coverage). The description adds context beyond the schema by listing the types of fields flagged, which helps the agent understand what 'audit for bloat' means. This incrementally improves parameter understanding.

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 'Identify and suggest' and the resource 'unnecessary fields for removal from a JSON payload'. It specifies the types of fields flagged (debug/temp/internal/deprecated, nulls, empty arrays, _ prefix), making the tool's purpose very specific and distinguishable from sibling tools like payload_analyze.

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 provided on when to use this tool versus alternatives (e.g., payload_analyze). The description only states what the tool does without contextual cues for decision-making.

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 provided, the description carries the full burden of behavioral disclosure. It accurately describes the analysis performed but does not mention any side effects, authentication requirements, rate limits, or constraints (e.g., payload size limits). The description is truthful but lacks additional behavioral context.

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, well-structured sentence that front-loads the purpose and lists key analytical metrics without superfluous words. Every word contributes meaning.

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 tool with one parameter and no output schema, the description covers the analysis scope comprehensively by listing the metrics computed. It does not specify the return format, but the described metrics implicitly define what the output contains. The tool is narrow and well-defined, so completeness is high.

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 single parameter 'payload' is described in the schema as 'JSON API response payload to analyze,' but the tool description adds significant semantic value by enumerating the analyses performed (field count, size, nesting depth, etc.). Since schema description coverage is 100%, the baseline is 3, and the extra context justifies a 4.

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 explicitly states the tool analyzes a JSON API response payload and lists specific metrics like field count, size, nesting depth, type distribution, null field count, and redundancy signals. It uses a specific verb ('Analyze') and identifies the resource ('JSON API response payload'), clearly distinguishing it from siblings that deal with bandwidth cost, comparison, and pruning.

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 does not provide any guidance on when to use this tool versus its siblings. It only describes what the tool does, leaving the agent to infer usage context without explicit when-to-use, when-not-to-use, or alternative recommendations.

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