Identify and suggest unnecessary fields for removal from a JSON payload. Flags debug/temp/internal/deprecated fields, null values, empty arrays, and _ prefix fields.
| Name | Required | Description | Default |
|---|---|---|---|
| payload | Yes | JSON payload to audit for bloat | |
| threshold | No | Importance threshold 0-100 (default 60). Fields scored below this are flagged. |
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.
Add one secure layer between your agents and this server.
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.