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 full behavioral burden. It implies a read-only network fetch and discloses the truncated return format, but it does not mention failure behavior (e.g., 404, non-robots content), redirects, size limits, or whether authentication is ever needed.
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 wasted words. It conveys the action, the target resource, and the precise scope of the return value efficiently.
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?
For a network-fetching tool with no annotations and no output schema, the description is thin: an agent cannot tell what 'group counts' look like, which of the four generic parameters to supply, or how errors are surfaced. The mismatch between the stated purpose and the parameter schema deepens the incompleteness.
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 description adds no parameter guidance. However, the schema parameter descriptions (e.g., 'JSON text to validate' and 'IANA timezone name') appear mismatched with a robots.txt fetching tool, and the description does nothing to clarify which parameters are actually relevant.
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 and resource ('Fetch a public robots.txt') and defines the return form ('return group counts only'), which clearly distinguishes it from a generic fetch tool and from the sibling 'citation'. It stops short of a 5 because 'group counts' is left undefined and no explicit differentiation from the sibling is stated.
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 on when to use this tool versus the sibling 'citation' or any alternative. The context is implied by the description, but there are no exclusions, prerequisites, or routing hints.
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.
json-ok and validate-json are effectively duplicate JSON validity checks, and several URL/HTTP tools (citation, normalize-url, domain-shape) have overlapping hostname/status concerns. compatibility is too vague to distinguish reliably from the other HTTP-related tools.
The names are all lowercase hyphenated, but they mix noun phrases (citation, timezone), imperative verbs (normalize-url, validate-json), and adjective constructions (json-ok). There is no consistent verb_noun or resource_action pattern, and the same JSON operation is called two different names.
Eleven tools is not inherently too large, but the set is not tightly scoped to the apparent 'JSON validity' purpose and bundles URL, robots, HTTP, and time utilities. The duplicate JSON validation tools also make the count feel padded rather than earning their places.
There are useful clusters for URL inspection, HTTP status, time, and JSON validity, but no clear workflow connects them and compatibility remains unexplained. Within a broad safe-web/utility interpretation the basics are present, yet obvious helpers such as header inspection or a single unified JSON validator are missing.