Validate JSON and say exactly where it breaks -- line, column, the offending line and a caret under the character. A byte offset is not something you can act on; a line and column is. Pass a path to read a value out instead.
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Optional. e.g. "a.b[1]" to read a value. | |
| text | Yes | The JSON to check. Capped at 64kb by the body limit. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses the exact output format (line, column, offending line, caret) and explains why byte offsets are avoided. It does not mention side effects or safety, but validation is inherently non-destructive. It could add more on return structure or error handling, but it covers the key behavior.
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 tight sentences with no fluff. The primary function and error-location detail are front-loaded, and the path usage is clarified in the second sentence. Every word earns its place.
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 validation tool with full schema coverage and no output schema, the description is largely complete. It specifies the exact error report format and the optional path. It could mention the 64kb limit (though in schema) and any error handling nuances, but nothing critical is missing for correct invocation.
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 the schema already documents both parameters with descriptions. The description's mention of path ('Pass a path to read a value out instead') repeats what the schema says, adding no new semantic detail. Baseline 3 applies since the 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 clearly states the tool validates JSON and reports the exact breaking location (line, column, offending line with caret). It also mentions an optional path to read a value, making the two modes distinct. This is a specific verb+resource and is not confused with any sibling tools.
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 explains when to use it (validate JSON, get precise error location) and hints at the path option for reading a value. It does not explicitly compare to alternatives or list exclusions, but the context is clear enough for an agent to decide. It lacks the explicit 'when not to use' guidance seen in top-tier examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.