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 burden of disclosure. It signals that the fetch is public and read-only and that only group counts are returned, which is useful behavioral context. It does not address errors, missing robots.txt files, or response format, so transparency is partial.
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?
One short, focused sentence with no filler. The action, target, and output limitation are stated clearly and up front, making the description efficient and easy to parse.
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 four parameters, none required, and no output schema, but the description does not explain which parameters are relevant or what the returned group counts look like. The mismatch between the robots.txt purpose and the generic schema fields leaves an agent uncertain about how to invoke this tool correctly.
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 baseline is 3 even though the tool description adds no parameter-specific detail. The schema's property descriptions are generic and not tailored to robots.txt, so the description does not clarify how url, host, json, or zone relate to the tool's purpose.
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 names a specific action ('Fetch'), a concrete resource ('robots.txt'), and an explicit output ('group counts only'), making it distinct from sibling tools like normalize-url or validate-json. An agent can tell exactly what this tool does from the first sentence.
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—fetching a public robots.txt and getting group counts—so an agent can infer when it is relevant. However, it never states when not to use this tool or mentions alternatives among the siblings.
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 the URL-related tools (citation, normalize-url, domain-shape) and time-related tools (utc-time, timezone, iana-zones) form overlapping clusters that an agent could misselect. Descriptions clarify the differences, but the boundaries are not immediately obvious.
All names use lowercase hyphenation, but the set mixes imperative verb_noun names (inspect-robots, normalize-url, validate-json) with noun/adjective names (citation, compatibility, timezone, base64-ok). This is readable but not a consistent naming pattern.
Eleven tools is a reasonable size for a general-purpose utility server and none seem redundant enough to cut. A few could be consolidated, such as timezone and utc-time, but the count is not excessive.
The server covers several utility categories, including URL analysis, time, JSON validation, HTTP status, and base64, but has noticeable gaps such as no base64 decode, no timezone conversion, and no URL encoding/decoding. These missing operations limit what an agent can accomplish without another server.