Fetch a public robots.txt and return group counts only.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | HTTPS URL to normalize or cite | |
| host | No | Public hostname | |
| json | No | JSON text to validate; discarded after the check | |
| zone | No | IANA timezone name |
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 full burden. It transparently discloses the core operation (an outbound fetch of a public file, read-only, no auth implied) and the reduced result shape. But it does not disclose failure modes (e.g., missing robots.txt, redirects) or define what 'group counts' means.
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 nine-word sentence that front-loads the verb and object and contains zero filler. 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?
The schema has four optional parameters that appear unrelated to robots.txt fetching, and there is no output schema or annotations. The description fails to clarify which parameter drives the fetch or what the returned group counts represent, so an agent would need to infer critical connection between the parameters and the stated purpose.
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 baseline is 3. The description adds no parameter context: it never indicates whether 'url' or 'host' is used to fetch robots.txt, nor what 'json' or 'zone' have to do with the operation, leaving the already-ambiguous schema uncompensated.
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 states a specific verb ('Fetch'), a concrete resource ('a public robots.txt'), and a precise output ('return group counts only'). The 'only' scopes the behavior and distinguishes it from a full-fetch or full-parse tool.
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 the use case: get group counts from a site's robots.txt. However, it names no alternatives, gives no 'when not to use' guidance, and doesn't position the tool relative to sibling tools like citation.
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.
Most tools have distinct outputs, but citation and normalize-url both return URL components and overlap on host/scheme. The time-related tools (iana-zones, timezone, utc-time) share a domain, and compatibility is vague enough that an agent may struggle to select it correctly.
All names are lowercase and kebab-case, which gives a consistent visual style. However, the naming mixes noun labels like citation and timezone with verb phrases like inspect-robots and validate-json, so there is no uniform verb_noun pattern.
Eleven tools is a reasonable count, but the server is named 'UUID Mint' and contains a loose collection of URL, time, HTTP, JSON, and UUID utilities. The count is not excessive, but the tools do not form a cohesive, well-scoped set.
The tool surface is a grab bag with no coherent domain, so common workflows are only partially covered. For example, there is UUID generation but no UUID validation, timezone lookup but no timezone conversion, and URL inspection but no full URL parsing or content retrieval.