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 provided, the description carries the behavioral disclosure burden. It clarifies that the tool returns only group counts, not the full file, and implies a read-only fetch of a public resource. It does not mention potential issues like network errors, redirects, or handling of missing robots.txt, which would be useful for a network-fetching tool.
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?
The description is a single, concise sentence that front-loads the core action and result. It contains no fluff or redundant information.
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 a simple fetch-and-count operation, and the description covers the main intent. However, with no output schema and no annotations, it lacks details about error behavior, response format, or why parameters like 'json' and 'zone' are accepted, which leaves gaps for an agent deciding whether and how to call it.
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 100%, so the schema already documents all parameters. However, the description adds only the context of 'public robots.txt' and 'group counts', which does not directly help map parameters like 'json' or 'zone' to the tool's purpose. The description does not compensate for the schema's generic parameter descriptions.
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 resource ('public robots.txt'), and a clear scope ('return group counts only'). This distinguishes it from generic fetch tools, though it does not explicitly name a sibling for comparison.
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 tool is for inspecting robots.txt files and returning only group counts, which gives some usage context. However, it does not explicitly state when to use this tool versus alternatives, nor when not to use it.
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.
The timezone tools overlap heavily: `timezone` already supports named IANA zones, making `utc-time` and `tz-america-new-york` largely redundant. The URL tools also blur together (`citation` vs `normalize-url` both return host/scheme information), and `compatibility` is too vague to confidently distinguish from the other request-classification tools.
All names share a lowercase hyphenated style, but they mix conventions: verb-prefixed tools like `inspect-robots`, `normalize-url`, and `validate-json` sit alongside noun phrases like `domain-shape`, `status-catalog`, and `utc-time`, plus single-word names like `timezone` and `citation`. The names are readable and predictable in format, but not in action pattern.
Eleven tools is not an extreme number, but the set is not well-scoped: it mixes time, URL normalization, HTTP status, robots.txt, and JSON validation concerns. The count feels like several small toolkits merged into one server rather than a focused tool collection where each tool clearly earns its place.
There is no coherent domain to call complete. As a clock server, it lacks timezone conversion, offsets, or date formatting; as a web utility server, it lacks common operations like fetching content or inspecting headers. Significant operations for either inferred purpose are missing, so agents would likely hit dead ends.