Validate a JSON string against a JSON Schema. Free. Returns validity and a list of violations.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | The JSON text to validate | |
| schema | Yes | The JSON Schema to validate against |
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?
No annotations are provided, so the description carries full burden. It discloses return of validity and violations, but does not mention safety, side effects, or error behavior. Adequate but not detailed.
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 sentences with no wasted words. First sentence states purpose, second adds return value and cost. Front-loaded and efficient.
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 no output schema, the description mentions return format (validity and violations). Sibling tools are distinct. Could specify format details, but acceptable for a simple tool.
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%, and the description does not add meaning beyond the existing parameter descriptions. Baseline 3 applies as 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 verb 'validate' and the resources 'JSON string' and 'JSON Schema'. It distinguishes from siblings like extract_json or repair_json by specifying validation as the core function.
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 for validation versus extraction or repair, but does not explicitly state when to use or not use this tool. However, sibling tool names provide context.
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 has a clearly distinct purpose: extract JSON from text, infer schema from data, repair malformed JSON, and validate against a schema. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case (extract_json, infer_schema, repair_json, validate_json), making them predictable and easy to distinguish.
With 4 tools, the server is well-scoped for its purpose of JSON handling. Each tool addresses a distinct need without excess or deficiency.
The tool set covers core JSON operations: extraction, schema inference, repair, and validation. Minor gaps exist (e.g., no transformation or generation), but the surface is largely complete for common tasks.