Analyze a JSON API response payload structure — field count, size in bytes, max nesting depth, type distribution, null field count, and redundancy signals (empty arrays, null values).
| Name | Required | Description | Default |
|---|---|---|---|
| payload | Yes | JSON API response payload to analyze |
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.
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Each tool targets a distinct aspect: cost estimation, comparison, pruning suggestions, and structural analysis. No two tools overlap in purpose, making misselection unlikely.
Names mostly follow a verb_noun pattern (compare_payloads, payload_analyze) with slight inconsistencies like 'bandwidth_cost_calc' (abbreviation) and 'field_prune_suggest' (two verbs). Overall predictable and readable.
Four tools is a well-scoped set for a payload auditing utility, covering analysis, comparison, cost, and suggestions without excess or deficiency.
The set covers analysis, comparison, and cost estimation, but lacks a tool to apply pruning or generate an optimized payload. However, comparison implies an external optimized version, so the gap is minor.