api-payload-auditor

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

No annotations are provided, so the description bears the burden. It states that the tool 'Returns cost at 25%, 50%, and 75% reduction scenarios,' which is useful. However, it does not disclose other behavioral aspects such as whether it is read-only, what assumptions are made, or how region affects pricing. This is adequate but not rich.

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 two concise sentences, front-loaded with the action verb 'Calculate'. It conveys purpose and return value without any fluff or repeated 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?

There is no output schema, so the description's mention of return scenarios ('25%, 50%, and 75% reduction scenarios') helps fill that gap. It also names the two key inputs (payload size and monthly traffic) and clarifies the tool's scope, making it reasonably complete for its complexity.

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?

All three parameters have descriptions in the schema, so coverage is 100%. The description mentions payload size and monthly traffic but does not add meaning beyond what the schema already provides. The baseline of 3 applies since schema does the heavy lifting.

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 the specific verb 'Calculate' and identifies the exact resource 'cloud API egress bandwidth costs'. It clearly distinguishes itself from sibling tools like payload_analyze and field_prune_suggest by focusing on cost estimation rather than payload analysis or 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 provides clear context for when to use the tool: to calculate egress bandwidth costs with reduction scenarios. It does not explicitly state when not to use it or name alternatives, but the context is sufficient given the tool's straightforward purpose.

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 must disclose behavior. It lists the return values (size reduction %, removed field paths, depth change), which is helpful, but it does not explicitly state whether the tool has side effects or modifies inputs, though the name suggests a read-only comparison.

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 concise: two sentences that immediately state the purpose and key outputs. No filler or redundant 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?

For a simple comparison tool with two parameters and no output schema, the description is complete enough. It specifies the key return data, which compensates for the lack of an output schema.

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 both parameters are already described clearly. The description adds little beyond the schema, but since the schema covers the parameters, a 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 function with a specific verb ('Compare') and resource ('original payload to an optimized version'), and the focus on comparison distinguishes it from sibling tools like field_prune_suggest and 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?

The description implies use after optimization by referencing 'original' and 'optimized' payloads, but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. No reference to sibling tools is made.

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 goes beyond the basic verb by explaining the detection logic: flags specific field types (debug/temp/internal/deprecated, null, empty arrays, _ prefix) and uses an importance threshold. However, it does not disclose whether the tool modifies the payload (though 'suggest' implies read-only) or what the output structure looks like, given there is no output schema.

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 two sentences, highly concise, and front-loaded with the primary purpose. Every word adds value, with no fluff or repetition of schema details. The structure is clear: first the overall goal, then the specific flags it identifies.

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 the tool has no output schema and no annotations, the description should ideally explain what the result looks like (e.g., list of suggested field names with reasons). It also lacks explicit usage guidance or alternative comparisons. While the core functionality is well covered, these gaps prevent it from being fully complete for an agent to select and invoke the tool without ambiguity.

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 input schema already provides full descriptions for both parameters: 'payload' is 'JSON payload to audit for bloat' and 'threshold' is 'Importance threshold 0-100 (default 60).' Since the schema coverage is 100%, the description adds minimal extra parameter meaning, only reinforcing the category list. Thus, 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 identifies the tool's purpose: 'Identify and suggest unnecessary fields for removal from a JSON payload.' It names specific categories of fields flagged (debug/temp/internal/deprecated, null, empty arrays, _ prefix), which distinguishes it from siblings like bandwidth_cost_calc and compare_payloads. The verb 'suggest' accurately reflects its analytical, non-mutating nature.

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 when you want to reduce JSON payload bloat, but it does not explicitly state when to use this tool versus alternatives like payload_analyze or compare_payloads. No exclusions, prerequisites, or alternative tool recommendations are provided. The context is clear but the guidance is implicit rather than explicit.

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 bears the full burden of disclosing behavior. It lists the computed metrics, which conveys a read-only analysis nature, but it does not explicitly state side effects, permission requirements, or edge-case handling. The absence of an output schema also leaves return format unspecified.

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 core purpose and then lists specific metrics. There is no redundant information or filler, making it highly concise.

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 lists the metrics computed, which partly compensates for the missing output schema, but it does not specify the exact return format, handling of edge cases (e.g., empty objects), or any limitations. Given the tool's simplicity, the description is adequate but has clear 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?

The input schema fully describes the single parameter 'payload' as an object, and schema description coverage is 100%. The tool description adds no extra semantics about the parameter itself beyond what the schema already states, 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 uses a specific verb ('Analyze') and clearly specifies the resource ('JSON API response payload structure') along with concrete metrics (field count, size in bytes, nesting depth, etc.). This makes it distinct from sibling tools like bandwidth_cost_calc or compare_payloads.

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 the tool is used for understanding payload structure, but it does not explicitly state when to choose this over siblings such as compare_payloads or field_prune_suggest. No exclusions or alternative contexts are mentioned.

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