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 behavioral burden. It usefully discloses that the fetch is public and read-only and that only group counts are returned, but it omits behavior around missing robots.txt files, network errors, rate limits, and why the parameters appear unrelated to fetching robots.txt. The mismatch between the description and the schema parameters is not explained.
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 front-loaded sentence with no redundancy, which is structurally good. However, it is underspecified for a tool with four parameters and no annotations, leaving critical invocation details unexplained.
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 has no annotations and no output schema, so the description must provide full context. It explains the return shape (group counts only) but omits what the counts represent, how errors like a missing robots.txt are handled, and which parameters are actually used. This is insufficient for reliable 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?
Although schema description coverage is 100%, the parameter descriptions are disconnected from the tool's stated purpose: 'url' is described as 'HTTPS URL to normalize or cite', and 'json' and 'zone' have nothing to do with fetching robots.txt. The tool description does not clarify which parameter identifies the target site, making it hard for an agent to invoke correctly.
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 the output ('group counts only'), so the tool's core purpose is clear. However, it does not differentiate itself from the sibling tool 'citation', and the schema parameters suggest broader unrelated behaviors, so it falls short of a 5.
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?
No guidance is given about when to use this tool versus the sibling 'citation' tool. The description merely says what the tool does, leaving the agent to infer that it should be used specifically for robots.txt inspection. There are no exclusions, prerequisites, or alternative routing.
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.
Several tools overlap around URL inspection (citation vs normalize-url) and time (timezone vs utc-time), though their descriptions clarify differing outputs. The vague compatibility tool adds some ambiguity about when it should be selected.
All names are lowercase and hyphenated, which is a consistent style, but the pattern mixes noun phrases like citation and timezone with verb-led names like normalize-url and validate-json. This is readable but does not follow one predictable convention.
Eleven tools is a reasonable size for this collection of privacy-preserving utility functions. The count is not bloated, though the time-related tools could arguably be consolidated.
The tool surface covers the apparent domain of inspecting data shapes without retaining content: URLs, email, JSON, robots.txt, time, and HTTP status. Minor gaps like direct header or DNS inspection exist, but common workflows are not dead-ended.