Transforms JSON data using a JQ-style filter expression, letting you reshape, extract, or restructure objects by passing input JSON and a filter instead of writing custom parsing code.
Transforms JSON data using a JQ-like filter
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Input JSON data | |
| filter | Yes | JQ-style filter expression |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false, so the safety profile is covered. Beyond that the description adds almost nothing: the qualifier 'JQ-like' hints at a dialect subset but never says which JQ features are unsupported, what errors look like for malformed filters, or what the output shape is.
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?
A single front-loaded sentence with no filler. It is efficient, though its brevity is partly under-specification rather than pure concision.
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 tool is simple (2 required params, no nesting in practice), but with no output schema the description should say what is returned — the filtered result, its type, or error behavior. That gap means an agent cannot fully predict the response shape before calling.
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%, with both 'input' and 'filter' described, so the schema carries the parameter burden. 'JQ-like filter' marginally narrows expectations for the filter parameter, but the description supplies no syntax examples or dialect constraints beyond the schema.
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?
States a specific verb ('Transforms') and resource ('JSON data') plus the mechanism ('JQ-like filter'), so an agent knows exactly what the tool does. It is trivially distinguishable from the file/echo siblings, though the description never explicitly contrasts them.
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 versus alternatives (e.g., file_read for pulling JSON off disk, or echo for passthrough). No preconditions, no exclusions, no mention of what happens on invalid filters. The agent must infer applicability entirely from the one-line purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.