jsonl_validate
Validate JSON Lines (NDJSON); optional per-line JSON Schema. When: Validate NDJSON / JSON Lines logs or datasets.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| schema | No | ||
| max_lines | No |
Validate JSON Lines (NDJSON); optional per-line JSON Schema. When: Validate NDJSON / JSON Lines logs or datasets.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| schema | No | ||
| max_lines | No |
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 present, so description carries full burden. It does not disclose error handling behavior, return format, whether validation stops at first error or accumulates, or performance implications. The description is too vague.
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, very concise. However, conciseness sacrifices critical details. The structure is clear but under-specified for a validation tool. Could be longer to cover essential behavior.
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?
Tool validates NDJSON with optional schema—medium complexity. No output schema, no error handling description, no mention of per-line behavior. Among many sibling validation tools (e.g., json_validate, csv_validate), description does not sufficiently complete the picture for an agent to use confidently.
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 description coverage is 0%. The description mentions 'optional per-line JSON Schema' which hints at the schema parameter, but does not explain the purpose of 'text' (expects JSON Lines?), 'max_lines' (default 200) is unmentioned. Adds minimal value beyond bare schema names.
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 Lines (NDJSON) with optional per-line JSON Schema. It gives a use case (validate logs or datasets) and distinguishes from general JSON validation tools like 'json_validate'.
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 mentions 'When: Validate NDJSON / JSON Lines logs or datasets' which implies usage context but does not provide explicit when-not-to-use or alternatives among sibling tools. No reasoning about trade-offs.
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.
Every tool has a clear, distinct purpose with thorough descriptions. Even closely related tools like base64_decode/encode and hash_md5/sha256 are easily differentiated by name and description.
All tools follow a consistent lowercase_underscore naming convention, typically in a <domain>_<action> or <action>_<domain> pattern. There are no jarring deviations or mixed styles.
193 tools is an extreme count, far beyond what any focused server needs. While each tool has utility, the sheer number creates a kitchen-sink effect that overwhelms agents and hinders discoverability.
Within each subdomain (JSON, cron, JWT, etc.), the coverage is exhaustive, covering validation, conversion, parsing, and more. Minor gaps exist (e.g., YAML-to-TOML conversion missing), but overall it is remarkably complete.